JP2019126035A - Image processing system, image processing method and program - Google Patents

Abstract

To provide an image processing system, an image processing method and a program obtaining a threshold matrix capable of reducing density unevenness of low frequency, while maintaining low granularity.SOLUTION: An image processing method includes a first filtering step of generating information indicating noise distribution state by applying a first filter to a dot pattern of some gray scale, a second filtering step of generating information indicating density unevenness by applying a second filter to a dot pattern of some gray scale, a dot pattern generation step of generating a dot pattern of a gray scale adjacent to some gray scale, by adding or deleting dots for the dot pattern, on the basis of the information indicating noise distribution state and the information indicating distribution state of density unevenness, and a determination step of determining the value of the threshold matrix, by applying the first filtering step, the second filtering step and the dot pattern generation step repeatedly with the initial dot pattern of a prescribed gray scale as the origin.SELECTED DRAWING: Figure 4

Description

  The present invention relates to quantization processing, and more particularly to a technique for reducing low-frequency density unevenness while maintaining low graininess in an image after quantization processing.

  Printing devices such as ink jet printers and laser printers generally apply quantization processing (pseudo-halftone processing) to an input image in order to obtain an image having the number of gradations that can be output by itself. The dither method is known as one of the quantization processing methods. The dither method compares the value of each pixel of the input image with a predetermined threshold, and the output value of each pixel is determined based on the result. Since the size of the threshold value matrix in which the threshold value is stored, which is used in the dither method, is generally smaller than the size of the input image, the output value of each pixel is determined by applying it periodically to the input image. There is.

  With regard to the dither method, for example, Patent Document 1 describes a method of generating a threshold value matrix that can realize low graininess with less noise in an image after quantization processing. The technology of Patent Document 1 called Void & Cluster method applies a low pass filter to a dot pattern to obtain a smoothed density image, and determines the coordinates to add / delete dots so as to suppress density fluctuation, thereby achieving low graininess. We have obtained a threshold matrix that realizes the However, in the case of the threshold value matrix generated by the Void & Cluster method, relatively low frequency density unevenness may occur in the image after quantization processing due to the deviation of the dot pattern in the threshold value matrix. The density unevenness is lower in frequency than the density fluctuation visually recognized as graininess, and easily recognized as a repetitive pattern in the application period of the threshold value matrix. In this regard, a technology has been proposed to reduce the above-mentioned uneven density by adding or deleting dots in consideration of the number of dots in each small section in the threshold value matrix (Patent Document 2). Specifically, dots are preferentially added to small areas having a small number of dots, and dots are preferentially deleted from small areas having a large number of dots, thereby eliminating bias in the dot pattern and improving density unevenness. There is.

USP 5535020 Patent No. 5472458 gazette JP, 2015-199214, A

  However, even in the quantization process to which the threshold value matrix obtained by using the method described in Patent Document 2 described above is applied, density non-uniformity at a visible level may be observed in the image after the process. In this case, the threshold matrix is divided into fixed subdivisions, and each subdivision is operated to eliminate the deviation of the number of dots, while the dot number in the subdivision and the number of dots crossing the subdivision It is because it can not eliminate up to the bias. As described above, it has been difficult to efficiently suppress both the graininess and the density unevenness in the prior art.

  A method of generating a threshold value matrix used for quantization processing according to the present invention includes a first filtering step of applying a first filter to a dot pattern of a certain gradation to generate information indicating a distribution state of noise; A second filtering step of applying a second filter to a dot pattern of a certain gradation to generate information indicating a distribution state of density unevenness, information indicating a distribution state of the noise, and a distribution state of the density unevenness A dot pattern generation step of generating a dot pattern of gradation adjacent to the certain gradation based on information; and the first filtering step, the second filtering step, and the dot starting from the dot pattern of a predetermined gradation The values of the threshold matrix are determined based on dot patterns of all gradations obtained by repeatedly applying a pattern generation process. A determining step, and having a.

  By using the threshold value matrix obtained by the method according to the present invention, it is possible to further reduce low frequency density unevenness while maintaining low graininess in the image after the quantization process.

A diagram showing an example of the configuration of a printing system (A) is a block diagram showing a hardware configuration of the information processing apparatus, (b) is a block diagram showing a hardware configuration of an image processing unit provided in the image forming apparatus Functional block diagram showing a software configuration for realizing the threshold value matrix generation unit according to the first embodiment A flowchart showing the entire flow of threshold matrix generation processing according to the first embodiment Flowchart showing a flow of a method of generating an initial dot pattern according to the first embodiment (A)-(f) is a figure explaining the effect of Embodiment 1. Functional block diagram showing software configuration for realizing the threshold value matrix generation unit according to the second embodiment A flowchart showing the entire flow of threshold matrix generation processing according to the second embodiment Flowchart showing a flow of a method of generating an initial dot pattern according to the second embodiment Functional block diagram showing software configuration for realizing the threshold value matrix generation unit according to the third embodiment A flowchart showing the entire flow of threshold matrix generation processing according to the third embodiment Functional block diagram showing software configuration for realizing the threshold value matrix generation unit according to the fourth embodiment A flowchart showing the entire flow of threshold matrix generation processing according to the fourth embodiment Functional block diagram showing software configuration for realizing the threshold value matrix generation unit according to the fifth embodiment A flowchart showing an entire flow of threshold matrix generation processing according to the fifth embodiment Diagram showing the variance ratio for each tone value Diagram showing an example of the results of evaluating the characteristics of multiple threshold matrices The figure explaining the effect of Embodiment 2 Diagram for explaining the effect of the third embodiment Diagram for explaining the effect of the fourth embodiment The figure explaining the effect of Embodiment 5 A diagram for explaining an example of a printing method using an inkjet method Functional block diagram showing software configuration for realizing the threshold value matrix generation unit according to the sixth embodiment A flowchart showing the entire flow of threshold matrix generation processing according to the sixth embodiment Flowchart showing a flow of a method of generating an initial dot pattern according to the sixth embodiment Diagram for explaining the effect of the sixth embodiment Diagram for explaining the effect of the sixth embodiment Diagram for explaining the effect of the sixth embodiment

  Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. In addition, the structure shown in the following embodiment is only an example, and this invention is not limited to the illustrated structure.

(Definition of terms)
In the following description of each embodiment, the generation process or the threshold matrix after generation is denoted as “M (x, y)”. The threshold matrix M (x, y) is a two-dimensional array, and x and y represent coordinates. The size of the threshold value matrix in the x direction is "Sx", and the size in the y direction is "Sy". In this embodiment, Sx is 256 pixels and Sy is 256 pixels. However, Sx = 256 pixels and Sy = 256 pixels are an example, and they can be of any size.

  In the present embodiment, as in the Void & Cluster method described above, the threshold matrix M (x, y) is generated by repeating dot addition to the initial dot pattern or repeating dot deletion. The dot pattern generated in this process is described as "d (x, y)". The dot pattern d (x, y) is a two-dimensional array, and its size is the same as M (x, y). The value of each pixel of the dot pattern d (x, y) is 1 when there is a dot, and 0 when there is no dot.

  The dot pattern d (x, y) changes in an iterative process of dot addition and dot deletion. That is, from the dot pattern with zero dot number, Sx × Sy + 1 kinds of dot patterns up to Sx × Sy dot number are generated in each process of repetition. Therefore, when the number of dots in the dot pattern d (x, y) is g, using g, it is possible to specify a certain point in the iterative process. In the following description, the dot pattern d (x, y) when the number of dots (= number of gradations) is g is simply described as “d (g)” with the coordinates x and y omitted.

Embodiment 1

(Configuration of printing system)
FIG. 1 is a diagram showing an example of the configuration of a printing system according to the present embodiment. The printing system illustrated in FIG. 1 includes an information processing apparatus 100 and an image forming apparatus 110. The information processing apparatus 100 includes a threshold value matrix generation unit 101, generates a threshold value matrix used in quantization processing (dither processing) by dithering, and supplies the threshold value matrix to the image forming apparatus 110. The information processing apparatus 100 also has a printer driver (not shown), and also has a function of giving a print instruction of an electronic document to be printed to the image forming apparatus 110. FIG. 2A is a block diagram showing the hardware configuration of the information processing apparatus 100. As shown in FIG. The information processing apparatus 100 includes a CPU 201, a ROM 202, a RAM 203, an HDD 204, and a network I / F 205. The CPU 201 controls the overall operation of the information processing apparatus 100 according to a program or the like. The RAM 203 is used as a main memory of the CPU 201 and a temporary storage area such as a work area. The HDD 204 is a large-capacity storage unit that stores various programs, a printer driver, and the like. The threshold value matrix generation unit 101 is realized by developing the corresponding control program stored in the HDD 204 or the like in the RAM, and the CPU executing this. A network I / F 205 is an interface that connects the information processing apparatus 100 to a network such as a LAN. The information processing apparatus 100 exchanges information such as sending data of a threshold matrix to the image forming apparatus 110 on the LAN using the network I / F 205.

  The image forming apparatus 110 includes an image processing unit 111 and an image forming unit 112. The image processing unit 111 performs quantization processing using the threshold value matrix generated by the information processing apparatus 100, and generates print image data that can be handled by the image forming unit 112. The image forming unit 112 performs printing on a recording medium such as paper by an inkjet method, for example, according to the generated print image data. FIG. 2B is a block diagram showing the hardware configuration of the image processing unit 111. As shown in FIG. The image processing unit 111 includes a color matching processing unit 211, a color separation processing unit 212, a gamma correction processing unit 213, a quantization processing unit 214, a CPU 215, a RAM 216, a ROM 217, and a communication unit 218. The CPU 215 centrally controls each processing unit described later and the whole based on a program stored in the ROM 217. The RAM 216 is used as a work area of the CPU 215. The communication unit 218 performs various communications with the information processing apparatus 100. For example, data of a generated threshold matrix and a print instruction are received via a network such as a LAN. The color matching processing unit 211 converts a color signal (RGB signal) representing the color of a multi-gradation input image sent from the information processing apparatus 100 into a device RGB signal that matches the color reproduction area of the image forming unit 112. Do. The color separation processing unit 212 converts the device RGB signals into CMYK signals representing cyan, magenta, yellow, and black, which are color materials used in the image forming unit 112, using a color separation table prepared in advance in the ROM 217. . The γ correction processing unit 213 performs correction such that the gradation value-density characteristics have a fixed relationship with respect to the CMYK signal, using the γ correction table that corrects the gradation value-density characteristics stored in the ROM 217. Convert to C'M'Y'K 'signal. The quantization processing unit 214 performs dither processing using the above-described threshold value matrix on the C′M′Y′K ′ signal of the input image to reduce the number of gradations of C′′M ′ ′ ′ ′ ′ Y′′K. Convert to a '' signal to generate a halftone image (halftone image). The above-described units (a color matching processing unit 211, a color separation processing unit 212, a γ correction processing unit 213, and a quantization processing unit 214) constituting the image processing unit 111 are capable of high-speed processing by being constituted by logic circuits.

(Generation of threshold matrix)
Next, a process of generating a threshold matrix in the information processing apparatus 100 will be described. FIG. 3 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. As shown in FIG. 3, the threshold value matrix generation unit 101 according to this embodiment includes a dot pattern generation unit 301, a first filter unit 302, a second filter unit 303, an addition unit 304, and a dot arrangement unit 305. Note that part or all of the elements constituting the threshold value matrix generation unit 101 may be realized by hardware (circuit). FIG. 4 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. The generation process of the threshold value matrix according to the present embodiment will be described in detail below with reference to these figures. In the following description, the symbol "S" represents a step.

In S401, the dot pattern generation unit 301 generates an initial dot pattern d (g ₀ ) having the number of gradations g = g ₀ . In the present embodiment, the number of gradations g ₀ of the initial dot pattern is Sx × Sx × 0.5 = 256 × 256 × 0.5 = 32768. Details of the method of generating the initial dot pattern will be described later.

S402 to S407 are processing of repeating addition of dots starting from the initial dot pattern d (g ₀ ) generated in S401 until the value of the number of gradations g reaches g _MAX . By this repetitive processing, the operation of adding one dot to the dot pattern having the gradation value of g and generating the dot pattern of the adjacent gradation (that is, the gradation value of g + 1) is repeated. Here, g _MAX g g ₀ , and in the present embodiment, g _MAX = Sx × Sy = 65536. A dot pattern of all gradations is generated by such repetitive processing of dot addition and repetitive processing of dot deletion (S409 to S414) described later.

  In S402, the first filter unit 302 applies a low pass filter having a filter coefficient k_n to the dot pattern d (g) corresponding to the gradation value g to extract noise components, and information indicating the distribution state of noise Generate This information indicating the distribution of noise (hereinafter referred to as “noise map n (g)”) is a two-dimensional array of the same size as the dot pattern d (g), the value of which varies with the gradation value g It is. With this noise map n (g), it is possible to evaluate the density of dots in the dot pattern d (g) having a tone value of g. For example, the smoothed density is lower as the value in the noise map n (g) is smaller, and the dot is evaluated as sparse. Conversely, it is evaluated that the smoothed density is higher and the dots are denser as the value in the noise map n (g) is larger.

  In step S403, the second filter unit 303 applies a low pass filter having a filter coefficient k_t to the dot pattern d (g) corresponding to the gradation value g to extract the density unevenness component, and the distribution state of the density unevenness is calculated. Generate the information shown. The information (hereinafter referred to as “density unevenness map t (g)”) indicating the distribution state of density unevenness is also a two-dimensional information of the same size as the dot pattern d (g) whose value changes according to the gradation value g It is an array. By this density unevenness map t (g), it is possible to evaluate lower frequency density unevenness in the dot pattern d (g) having a gradation value of g. For example, it is evaluated that the density is lower as the value in the density unevenness map t (g) is smaller, and conversely, the density is evaluated as higher as the value t (g) is larger.

  Here, the filter coefficient k_n and the filter coefficient k_t used in the above-described S402 and S403 will be described. The threshold matrix is assumed to be periodically applied to the input image. In the filtering process of both steps, as in Patent Document 1, the noise component and the uneven density component are extracted by the cyclic convolution operation of the dot pattern d (g) and the filter coefficient to be used. The cyclic convolution operation is an operation for performing a normal convolution operation between the dot pattern d (g) for which periodic boundary conditions are set and the filter coefficient. The filter coefficient k_n and the filter coefficient k_t both have a two-dimensional array, and in the present embodiment, have the same size as the dot pattern d (g). That is, when the filter size in the x direction is Sfx and the filter size in the y direction is Sfy, Sfx = Sfy = 256. The filter coefficient k_n and the filter coefficient k_t are expressed by the following equations (1) and (2), respectively.

In the above equations (1) and (2), (x, y) represents coordinates. x ₀ and y ₀ represent the center coordinates of the filter coefficient, and x ₀ = Sfx ÷ 2, y ₀ = Sfy ÷ 2. Further, σ _n is a parameter that defines the frequency characteristics of k_n (x, y), and σ _t is a parameter that defines the frequency characteristics of K_t (x, y). The routine for adding dots aims at finally achieving low graininess by adding dots in a portion where the dot density is coarse. In order to preferably realize this, it is necessary to extract frequency components that are visually noticeable as graininess from the dot pattern d (g). It is known that in a general printed matter, the frequency component noticeable as graininess is about 6 cycles / mm or less and the frequency component visually noticeable as density unevenness is about 0.75 cycles / mm or less. For example, when the printing resolution is set to 1200 dpi, for example, about 1.8 may be used as the value of σ _n for appropriately extracting each of the above-mentioned frequency bands, and about 8.5 may be used as the value of σ _t . The filter coefficients used in each step are not limited to the examples of the above equations (1) and (2), as long as they can extract frequency components that are noticeable as graininess. Usually, the filter size is determined by the pass band, so when the printing resolution is 1200 dpi, the filter used for noise component extraction is a low pass filter of about 9 pixels × 9 pixels, and the filter used for extraction of density unevenness is 65 pixels × 65 pixels It may be a low pass filter of some degree. Also, in these filtering processes, multiplication in frequency space may be used instead of the cyclic convolution operation. It returns to description of the flow of FIG.

In S404, dots are added to the dot pattern d (g) based on the noise map n (g) generated in S402 and the density unevenness map t (g) generated in S403. This process is performed by the addition unit 304 and the dot arrangement unit 305. First, the adding unit 304 combines the noise map n (g) and the density unevenness map t (g). That is, the sum of the noise map n (g) and the density unevenness map t (g) is obtained. The obtained combination result (hereinafter, described as “n (g) + t (g)”) is output to the dot placement unit 305. The dot placement unit 305 uses the n (g) + t (g) received from the addition unit 304 to perform a process of adding a dot to the portion with the lowest dot density. Specifically, among pixel positions (pixel value = 0) at which dots in the dot pattern d (g) do not exist, the noise map n (g) at the coordinates (x, y) and the density unevenness map t (g) First, the pixel position (x _MIN , y _MIN ) at which the sum is minimized is searched. Then, the pixel value of the found pixel position (x _MIN , y _MIN ) is changed from 0 to 1. If a plurality of pixel positions at which the value of n (g) + t (g) is the smallest are found, the pixel value of the pixel position (x _MIN , y _MIN ) at which the value of t (g) becomes the smallest Set it to 1. Furthermore, when there are a plurality of positions at which the value of t (g) is minimum, the position to which the dot is added may be randomly selected from among them.

In S405, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MIN , y _MIN ) to which the dot is added. Specifically, the current value of g is set as the value of M (x _MIN , y _MIN ) in the threshold matrix M (x, y). In the following S406, the value of the gradation number g is incremented, and g = g + 1.

In S407, it is determined whether the value of the number of gradations g has reached g _MAX (here, 65536). If it is not g = g _{MAX as} a result of a judgment, it will return to S402 and processing will be continued. On the other hand, if g = g _MAX , the process proceeds to S408.

In S408, the initial dot pattern d (g ₀ ) in which the gradation value generated in S401 is g ₀ = 32768 is read. The subsequent S409 to S414 are processing of repeating deletion of dots starting from the read initial dot pattern d (g ₀ ) until the value of the gradation number g reaches g _MIN . By this repetitive processing, the operation of deleting one dot from the dot pattern having the gradation value of g and generating the dot pattern of the adjacent gradation (that is, the gradation value of g-1) is repeated. . Here, g _MIN ≦ g ₀ , and in the present embodiment, g _MIN = 0. A dot pattern of all gradations is completed by combining the result of the subsequent iterative process of dot deletion and the result of the iterative process of dot addition obtained up to S407. In the following, the iterative process of dot deletion will be described focusing on the difference from the iterative process of dot addition.

  S409 is the same as S402, and the first filter unit 302 generates the above-described noise map n (g). The subsequent S410 is the same as S403, and the second filter unit 303 generates the density unevenness map t (g).

In step S411, the dot pattern d (g) is deleted based on the noise map n (g) generated in step S409 and the density unevenness map t (g) generated in step S410. That is, the dot arrangement unit 305 uses the n (g) + t (g) output result of the addition unit 304 to delete dots from the portion with the highest dot density. Specifically, among pixel positions where dots in the dot pattern d (g) exist (pixel value = 1), noise map n (g) and density unevenness map t (g) at the coordinates (x, y) First, the pixel position (x _MAX , y _MAX ) at which the sum is maximized is searched. Then, the pixel value of the found pixel position (x _MAX , y _MAX ) is changed from 1 to 0. In addition, when a plurality of pixel positions where the value of n (g) + t (g) is the largest are found, the pixel value of the pixel position (x _MAX , y _MAX ) where the value of t (g) becomes the maximum among them. You can set 0 to 0. Furthermore, when there are a plurality of positions where the value of t (g) is maximum, it is sufficient to randomly select the position from which the dot is to be deleted.

In S412, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MAX , y _MAX ) at which the dot is deleted. Specifically, the current value of g is set as the value of M (x _MAX , y _MAX ) in the threshold matrix M (x, y). In the following S413, the value of the number of gradations g is decremented to g = g-1. Then, in S414, it is determined whether the value of the number of gradations g has reached g _MIN (here, 0). If it is not g = g _{MIN as} a result of determination, it will return to S409 and processing will be continued. On the other hand, if g = g _MIN , the process proceeds to S415.

At S415, the range adjustment of the threshold in the threshold matrix is performed according to the range of the pixel value of the input image. At the stage where the processing has advanced to this step, values of g _MIN to g _MAX (0 to 65536 in the present embodiment) are stored in the threshold value matrix M (x, y). When the signal value of the input image at the time of performing the dither processing is, for example, 8 bits, the range of the input image is 0 to 255, so it is appropriate to use a threshold matrix in which values from 0 to 65536 are stored. The quantization result can not be obtained. Therefore, in this step, the range of values of the threshold value matrix M (x, y) is adjusted so as to match the number of bits of the signal value of the input image. For example, when it is desired to change the range of values of the threshold matrix from th _MIN to th _MAX , the value of the adjusted threshold matrix is determined by a × M (x, y) + b. However, it is _{a = (th MAX -th MIN)} ÷ (g MAX -g MIN), b = th MIN -a × g MIN. In addition, the method of range adjustment may use not only this method but the method of patent document 1, for example.

  The above is the entire flow of the threshold value matrix generation process according to the present embodiment.

(How to generate initial dot pattern)
In the present embodiment, first, a random dot pattern is generated, and the movement of dots is repeated starting from this random dot pattern, whereby an initial dot pattern d (g ₀ ) with low granularity and inconspicuous density unevenness is obtained. Generate Hereinafter, the method of generating the initial dot pattern according to the present embodiment will be described in detail with reference to the flowchart of FIG. In the following description, the symbol "S" represents a step.

In S501, a random dot pattern d_r of gradation number g = g ₀ is generated. Specifically, starting from an image having no dots at all, dots are repeatedly added until the number of gradations reaches g ₀ (in this embodiment, g ₀ = 32768) to generate a random dot pattern Do. The candidate of the position to add a dot generates a random number of 0 to Sx, for example, to determine the position x_r in the x direction, and similarly generates a random number of 0 to Sy to determine the position yr in the y direction. Then, when there is no dot at the position of the determined coordinates (x_r, y_r), a dot is added to the position (the pixel value is changed from 0 to 1). In this case, if the pixel value at the pixel position has already become 1, a random number is newly generated to change the candidate of the position to which the dot is added. The above process is repeated to obtain a random dot pattern d_r.

In S502, with respect to the random dot pattern d_r number of gradations g ₀ generated in S501, to apply the same process as S402 described above, to generate a noise map N_r. In S503, the same processing as S403 described above is applied to the random dot pattern d_r to generate a density unevenness map t_r.

Then, in S504, processing is performed to move the dots based on the result of combining the noise map n_r and the density unevenness map t_r. Specifically, it is as follows. First, the position (x _MAX , y _MAX ) of a pixel at which the value of n_r + t_r is maximized is searched from among the pixels in the current random dot pattern d_r in which dots are present. Next, the position (x _MIN , y _MIN ) of the pixel at which the value of n_r + t_r is the smallest is searched from among the pixels in the current random dot pattern d_r where no dot exists. Then, the dot at the position of the coordinate (x _MAX , y _MAX ) is moved to the position of the coordinate (x _MIN , y _MIN ). That is, the pixel value at the position of the coordinate (x _MAX , y _MAX ) is set to 0, and the pixel value at the position of the coordinate (x _MIN , y _MIN ) is set to 1.

In S505, it is determined whether or not each process from S502 to S504 has been performed a predetermined number of times. The predetermined number of times is arbitrary, for example, 100 times, but as in Patent Document 1, a predetermined convergence condition may be set. If the number of executions has reached the predetermined number as a result of the determination, the present process is ended. Then, the random dot pattern d_r at the end of the process becomes the initial dot pattern d (g ₀ ). On the other hand, if the number of executions has not reached the predetermined number, the process returns to S502 and the process is continued.

The initial dot pattern d (g ₀ ) of the present embodiment is generated by the process as described above.

(Effect of this embodiment)
Here, the effects of the present embodiment will be described in detail. (A) to (f) of FIG. 6 are diagrams for explaining the effect of the present embodiment, and an example of adding one dot is shown for convenience in a one-dimensional space. FIG. 6A shows a dot pattern d at a certain gradation number g.

  First, the prior art will be described. In the technique of Patent Document 1, the addition of dots is performed based only on the evaluation value of noise in the dot pattern. FIG. 6 (b) shows a noise map n generated from the dot pattern d of (a) and showing the distribution of noise. In the technique of Patent Document 1, dots are added at positions where the value of the noise map n is minimum. In the case of the noise map n shown in FIG. 6B, a dot is added at the position of x1 which is the minimum value. However, the dot interval is wider at the periphery of X2, which has the next smallest value, than at the periphery of X1, which is the minimum value. This means that dots should be added not at the X1 position but at the X2 position in order to reduce low frequency density unevenness. In the technique of Patent Document 1, since the additional position of the dot is determined based only on the evaluation value of noise, even in the situation as illustrated in FIG. 6B, the dot can not be added to the position of X2. As a result, uneven density becomes noticeable.

  In this respect, in the technology of Patent Document 2, the density unevenness is expressed by the number of dots c in each small section, and the dot arrangement in consideration of the number of dots c is performed to reduce the density unevenness. FIG. 6C shows the number c of dots in each small area corresponding to the dot pattern d in FIG. 6A (in this example, the number of dots in the small area on the left is 4 and the number of dots in the small area on the right Shows 3). In the technique of Patent Document 2, dots are added at positions where the sum of the number c of dots per small section and the noise map n is minimum. FIG. 6 (d) shows the sum of the noise map n of FIG. 6 (b) and the number of dots in each of the small sections shown in FIG. 6 (c). In this case, since the minimum value of the sum is at the position of X2 and dots are added at the position of X2 instead of X1, the problem of Patent Document 1 can be solved. However, with the number c of dots in each small section shown in FIG. 6C, the unevenness of dots in each small area can not be taken into consideration, and this unevenness may be visually recognized as uneven density. For example, it is assumed that the division positions of the small sections become as shown in FIG. 6 (c '). Comparing FIG. 6C and FIG. 6C, the number of dots in the left section changes from 4 to 3 and the number of dots in the right section changes from 3 to 4 There is. When the dot number c for each small section is as shown in FIG. 6 (c ′), the sum with the noise map n is as shown in FIG. 6 (d ′). Since the minimum value in this case is at the position of X1 instead of X2, a dot is to be added at the position of X1 instead of X2. As a result, uneven density is not eliminated but rather intensified. As described above, in the technique of Patent Document 2, even if the dot pattern is the same, the uneven density may not be reduced depending on the division position of the small section. This is because the technique of Patent Document 2 can not take into consideration the density of dots in the small section or the density of dots between the small sections.

  On the other hand, in the method of the present embodiment, the density unevenness is evaluated not by the dot number c for each small section, but by the density unevenness map t expressing the low frequency component of the dot pattern d. When the density unevenness is evaluated by the low frequency component, the evaluation value is determined according to the relative dot position in the filter range. That is, if the dot arrangement in the filter is the same, the value of the low frequency component is the same regardless of the position in the threshold value matrix. Therefore, in the dot arrangement using the density unevenness map t representing the low frequency component, the density unevenness can be reduced more reliably than in the technique of Patent Document 2. FIG. 6E shows a density unevenness map t showing the distribution state of density unevenness extracted from the dot pattern d in FIG. 6A. In the method of this embodiment, the sum of the noise map n shown in FIG. 6B and the density unevenness map t shown in FIG. 6E is obtained, and a dot is added at a position where the value of the sum is minimum. FIG. 6 (f) shows the sum of the noise map n of FIG. 6 (b) and the density unevenness map t of FIG. 6 (e). In the case of FIG. 6F, the minimum value of the sum is the position of X3 close to X2, and a dot is added to this position of X3. As described above, it can be seen that dots can be added to the position of X3 that is most effective for reducing density unevenness in the method of the present embodiment.

  Furthermore, in the method of the present embodiment, when extracting low frequency components representing uneven density from the dot pattern d, a low pass filter based on visual sensitivity is used. Therefore, the density unevenness map t generated in the above-described S403 and S410 is closer to the density unevenness perceived by humans than the dot number c for each small section. By performing the dot arrangement using such density unevenness map t, it is possible to more effectively reduce the visually recognized density unevenness.

(Modification)
In the present embodiment, the threshold value matrix generation unit 101 having the configuration of FIG. 3 performs the dot deletion repetitive processing after the dot addition repetitive processing is performed, but the present invention is not limited to such a configuration. For example, two threshold matrix generation units 101 may be provided, and after the initial dot pattern d (g ₀ ) is generated in S401, the dot addition in S402 to S407 and the dot deletion in S409 to S414 may be processed in parallel.

Further, the sum of the noise map n (g) and the density unevenness map t (g) generated in the above-described S404 and S411 may be a weighted sum in which a weight is added. For example, when it is desired to create a threshold value matrix in which the effect of suppressing density unevenness is increased, the weight of the density unevenness map t (g) is increased, and dots are added and deleted in the direction to further alleviate density unevenness. do it. For example, consider the case _where the value of σ _n is about 1.8 and the value of σ _t is about 8.5, using the above equations (1) and (2) as the filter coefficient k_n and the filter coefficient k_t. In this case, a suitable threshold value matrix can be obtained by setting the weight W of the density unevenness map t (g) to the noise map n (g) to about 2.5.

  In order to achieve both noise and density unevenness, the distribution Var_n (g) of the noise map n (g) is equal to the distribution Var_t (g) of the density unevenness map t (g) after multiplication by the weight W. Is desirable. Here, “dispersion ratio F (g) = Var_t (g) ÷ Var_n (g)” is defined.

  Temporarily, in the case of "Var_n (g) >> Var_t (g)" (that is, in the case of "F (g) << 1"), the dot arrangement which considered density unevenness map t (g) can not be performed. On the contrary, in the case of "Var_n (g) << Var_t (g)" (that is, in the case of "F (g) >> 1"), the dot arrangement in consideration of the noise map n (g) can not be performed.

  Therefore, it is preferable to determine the weight W so that Var_n (g) and Var_t (g) become substantially the same. Specifically, it is desirable to determine the weight W such that the dispersion ratio F (g) is 0.5 or more and 10 or less at almost all tone values g except for the minimum tone value and the maximum tone value. . The specific example of the determination method is shown below. It is known that the dispersion ratio F (g) at the tone value g changes depending on the dot pattern to be generated. Therefore, the weight W is changed in multiple steps to generate a threshold matrix of a plurality of patterns, and the dispersion ratio F (g) when given a certain weight is first confirmed. For example, a threshold matrix when the weight W is 1 and a threshold matrix when the weight W is 2.5 and a threshold matrix when the weight W is 10 are generated, and the dispersion ratio F (g) in each case is calculated. Check. Then, the weight W when the dispersion ratio F (g) falls within the range of 0.5 or more and 10 or less is adopted as an appropriate weight.

FIG. 16 uses the above equations (1) and (2) as the filter coefficient k_n and the filter coefficient k_t, the value of σ _n is about 1.8, the value of σ _t is about 8.5, and the weight W is 2 15 is a graph showing the dispersion ratio F (g) for each gradation value g when the value is set to about 0.5. In the graph of FIG. 16, the dispersion ratio is in the range of 0.5 or more and 10 or less at almost all tone values g except for the minimum tone value and the maximum tone value. Thus, the weight W = 2.5 in this case is an appropriate weighting factor. Furthermore, the weight W may be changed for each gradation value g. For example, let W (g) be the weight for the tone value g, and let F_0 (eg, F_0 = 5.5) be the desired dispersion ratio. At this time, by setting “weight W (g) = (F_0 × Var_n (g) ÷ Var_t (g)) ^0.5 ”, desired dispersion is obtained in all gradations except the minimum gradation value and the maximum gradation value. The ratio F_0 can be realized. FIG. 17 is a graph showing an example of the result of evaluation of the characteristics of a plurality of threshold value matrices generated in such a manner that the dispersion ratio has a desired value. In addition, the above equation (1) and equation (2) are used as the filter coefficient k_n and the filter coefficient k_t, the value of σ _n is about 1.8, and the value of σ _t is about 8.5.

  In the graph of FIG. 17, the horizontal axis is the dispersion ratio F (g), and the values of the respective dispersion ratios correspond to one threshold matrix. The vertical axis is an evaluation value of the characteristics of the threshold matrix. Specifically, using the threshold matrix to be evaluated, dither processing is applied to the uniform patch image of 33 gradations, and the evaluation value of the generated halftone image is obtained for each patch, and each evaluation value is obtained. Is an averaged value. The evaluation value of the halftone image indicates that the uneven density or the noise is more noticeable as the evaluation value of the degree of the uneven density appears and the evaluation value of the degree of the noise is greater. The evaluation value of the degree to which the density unevenness is noticeable is a feature amount related to the variation of the image in which the band in which the density unevenness is noticeable is convoluted with the halftone image to be evaluated. The evaluation value of the degree to which noise is noticeable is a feature amount related to the amount of fluctuation of an image obtained by convoluting a filter in a band in which noise is noticeable in the halftone image to be evaluated. According to subjective evaluation experiments conducted by the inventor, it is known that noise and density unevenness are not noticeable if both of these two evaluation values are 0.17 or less.

  In the evaluation result of FIG. 17, if the dispersion ratio is 0.5 or more and 10 or less, these two evaluation values become 0.17 or less, which can achieve both suppression of noise and suppression of uneven density. On the other hand, when the dispersion ratio is less than 0.5, dot arrangement in consideration of the density unevenness map t (g) can not be performed, and the density unevenness evaluation value is worse than 0.17. In addition, when the dispersion ratio exceeds 10, dot arrangement in consideration of the noise map n (g) can not be performed, and the noise evaluation value is worse than 0.17. Thus, by determining the weight W so that the dispersion ratio F (g) is 0.5 or more and 10 or less, both suppression of noise and suppression of uneven density can be achieved. Note that the index for determining the weight W is not limited to the variance ratio F (g). For example, the ratio R (g) of the feature amount related to the fluctuation of the noise map n (g) and the density unevenness map t (g) may be used instead of the dispersion ratio F (g). At this time, assuming that the feature amount related to the variation of the noise map n (g) is C_n (g) and the feature amount related to the variation of the density unevenness map t (g) is C_t (g), the ratio R (g) of the feature amounts is C_t (g) ÷ C_n (g). In addition, as the feature amount indicating a change, a standard deviation, an amplitude, or the like may be used.

  As described above, according to this embodiment, it is possible to obtain a threshold value matrix capable of further effectively reducing low frequency density unevenness while maintaining low graininess.

Embodiment 2

  In the first embodiment, it is possible to reduce both graininess and density unevenness by arranging dots in consideration of both noise and density unevenness. However, depending on the tone value, uneven density may not be noticeable. In this case, it is possible to realize low granularity by arranging dots in consideration of only noise, rather than arranging dots in consideration of both noise and density unevenness. Therefore, a second embodiment will be described as an aspect of performing dot arrangement in which only noise is taken into consideration when it is determined that the density unevenness is not noticeable, paying attention to the fluctuation amount of the density unevenness. The description of the portions common to the first embodiment will be omitted or simplified, and in the following, differences will be mainly described.

  FIG. 7 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. The threshold value matrix generation unit 101 of this embodiment includes a dot pattern generation unit 701, a first filter unit 702, a second filter unit 703, an uneven density variation calculation unit 704, a comparison unit 705, a selection unit 706, and a dot arrangement unit 707. Configured FIG. 8 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. The generation process of the threshold value matrix according to the present embodiment will be described in detail below with reference to these figures. In the following description, the symbol "S" represents a step.

In S801, the dot pattern generation unit 701 generates an initial dot pattern d (g ₀ ) whose tone value is g ₀ . As in the first embodiment, the gradation number g ₀ of the initial dot pattern is Sx × Sx × 0.5 = 256 × 256 × 0.5 = 32768. However, the method of generating the initial dot pattern is different from that of the first embodiment. Details of the method of generating the initial dot pattern according to this embodiment will be described later.

S802 to S809 are processes corresponding to S402 to S407 in the first embodiment, and until the value of the number of gradations g reaches g _MAX starting from the initial dot pattern d (g ₀ ) generated in S801 It is an iterative process. The difference from the first embodiment is that S804 and S805 are added. Also in the present embodiment, g _MAX gg ₀ and g _MAX = S x × S y = 65536.

  In S802, the first filter unit 702 applies a low pass filter having a filter coefficient k_n to the dot pattern d (g) corresponding to the gradation value g to extract noise components, and indicates noise distribution state Generate map n (g). This process is the same as S402 of the first embodiment. In S803, the second filter unit 703 applies a low pass filter having a filter coefficient k_t to the dot pattern d (g) corresponding to the gradation value g to extract the density unevenness component, and the distribution state of the density unevenness To generate a density unevenness map t (g) indicating. This process is the same as S403 of the first embodiment.

In S804, it is determined based on the density unevenness map t (g) generated in S803 whether the fluctuation amount v_t (g) of density unevenness is larger than a predetermined value th_t set in advance. This determination process is performed as follows by the density unevenness variation calculation unit 704 and the comparison unit 705. First, the uneven density fluctuation amount calculation unit 704 calculates the uneven density fluctuation amount v_t (g). Here, v_t (g) is a scalar quantity, and in this embodiment, the difference between the maximum value and the minimum value in the density unevenness map t (g) is used. That is, when the maximum value of t (g) is t _MAX (g) and the minimum value is t _MIN (g), a value obtained by subtracting t _MIN (g) from t _MAX (g) is used as v_t (g) . Note that the amplitude may be used instead of the difference between the maximum value and the minimum value, and for example, the variance or standard deviation of t (g) may be v_t (g). Next, the comparison unit 705 compares v_t (g) with a predetermined value th_t. The predetermined value th_t is a value serving as a reference for determining whether or not the density unevenness is noticeable, and is set in advance according to the desired characteristics of the threshold value matrix. As a result of the determination, if the density unevenness fluctuation amount v_t (g) is smaller than the predetermined value th_t (if the judgment result is false), the process proceeds to S805, and the density unevenness fluctuation amount v_t (g) is larger than the predetermined value th_t ( If the determination result is true), the process advances to step S806.

In S805, dots are added to the dot pattern based on only the noise map n (g). This processing is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S804 is false, the selection unit 706 outputs the noise map n (g) input from the first filter unit 702. The dot placement unit 707 performs a process of adding dots to the portion with the lowest dot density, using the noise map n (g). Specifically, among pixel positions (pixel value = 0) at which dots in the dot pattern d (g) do not exist, pixel positions at which the value of the noise map n (g) at the coordinates (x, y) becomes minimum First, search for (x _MIN , y _MIN ). Then, the pixel value of the found pixel position (x _MIN , y _MIN ) is changed from 0 to 1.

  In S806, dots are added to the dot pattern based on the noise map n (g) and the density unevenness map t (g). This processing is also performed by the selection unit 706 and the dot placement unit 707. If the determination result in S804 is true, the selection unit 706 combines the noise map n (g) input from the first filter unit 702 and the density unevenness map t (g) input from the second filter unit 703, The result n (g) + t (g) is output. The dot placement unit 707 uses this n (g) + t (g) to perform a process of adding a dot to the portion with the lowest dot density. This process is the same as S404 of the first embodiment.

In S807, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MIN , y _MIN ) to which the dot is added. In the subsequent S808, the value of the gradation number g is incremented, and g = g + 1. Then, in S809, it is determined whether the value of the number of gradations g has reached g _MAX (here, 65536). If it is not g = g _{MAX as} a result of determination, it will return to S802 and processing will be continued. On the other hand, if g = g _MAX , the process proceeds to S810. These processes are the same as S405 to S407 in the first embodiment.

In S810, the initial dot pattern d (g ₀ ) in which the gradation value generated in S801 is g ₀ = 32768 is read. The subsequent S811 to S818 are processing of repeating deletion of dots starting from the read initial dot pattern d (g ₀ ) until the value of the gradation number g reaches g _MIN . The difference from the first embodiment is that S813 and S814 are added. Also in the present embodiment, g _MIN ≦ g ₀ and g _MIN = 0. In the following, the iterative process of dot deletion will be described focusing on the difference from the iterative process of dot addition.

  S811 is the same as S802, and the first filter unit 702 generates the above-described noise map n (g). The subsequent S812 is the same as S803, and the second filter unit 803 generates a density unevenness map t (g).

  In S813, as in S804, it is determined based on the density unevenness map t (g) generated in S812 whether or not the variation amount of density unevenness v_t (g) is larger than a predetermined value th_t set in advance. The contents of this determination process are as described in S804. As a result of the determination, if the density unevenness fluctuation amount v_t (g) is smaller than the predetermined value th_t (if the judgment result is false), the process proceeds to S814, and the density unevenness fluctuation amount v_t (g) is larger than the predetermined value th_t ( If the determination result is true), the process advances to step S815.

In S814, the dot pattern is deleted based on only the noise map n (g). This processing is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S813 is false, the selection unit 706 outputs the noise map n (g) input from the first filter unit 702. The dot placement unit 707 uses this noise map n (g) to delete dots from the portion with the highest dot density. Specifically, among pixel positions (pixel value = 1) at which dots in the dot pattern d (g) exist, pixel positions at which the value of the noise map n (g) at the coordinates (x, y) is maximum First search for (x _MAX , y _MAX ). Then, the pixel value of the found pixel position (x _MAX , y _MAX ) is changed from 1 to 0.

  In S815, the dot pattern d (g) is deleted based on the noise map n (g) generated in S811 and the density unevenness map t (g) generated in S812. This processing is also performed by the selection unit 706 and the dot placement unit 707. If the determination result in S813 is true, the selection unit 706 combines the noise map n (g) input from the first filter unit 702 and the density unevenness map t (g) input from the second filter unit 703, The result n (g) + t (g) is output. The dot placement unit 707 uses this n (g) + t (g) to delete dots from the portion with the highest dot density. This process is the same as S411 of the first embodiment.

In S816, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MAX , y _MAX ) at which the dot is deleted. In the following S817, the value of the gradation number g is decremented to g = g-1. Then, in S818, it is determined whether the value of the number of gradations g has reached g _MIN (here, 0). If it is not g = g _{MIN as} a result of determination, it will return to S811 and processing will be continued. On the other hand, if g = g _MIN , the process proceeds to S819. These processes are the same as S412 to S414 of the first embodiment.

  In S819, the range adjustment of the threshold in the threshold matrix is performed according to the range of the pixel value of the input image. This process is the same as S415 of the first embodiment.

  The above is the entire flow of the threshold value matrix generation process according to the present embodiment.

(How to generate initial dot pattern)
The method of generating the initial dot pattern according to the present embodiment will be described in detail with reference to the flowchart of FIG. In the following description, the symbol "S" represents a step.

First, in S901, it generates a random dot pattern d_r gradation depth g = g _0. Subsequently, in S902, with respect to the random dot pattern d_r number of gradations g ₀ generated in S901, to apply the same process as S802 described above, to generate a noise map N_r. In the subsequent S903, the same processing as the above-described S803 is applied to the random dot pattern d_r to generate the density unevenness map t_r. The processing so far is the same as S501 to S503 in the first embodiment.

  In S904, it is determined based on the density unevenness map t generated in S903 whether the fluctuation amount v_t of density unevenness is larger than a predetermined value th_t set in advance. This determination process is the same as S804 and S813 described above. As a result of the determination, if the uneven density variation amount v_t is smaller than the predetermined value th_t (if the determination result is false), the process proceeds to S905, and if the uneven density variation amount v_t is larger than the predetermined value th_t (if the determination result is true) ) Goes to S906.

In S905, the dot is moved based on only the noise map n_r. Specifically, it is as follows. First, among the pixels in the current random dot pattern d_r in which dots are present, the position (x _MAX , y _MAX ) of the pixel at which the value of n_r is maximum is searched. Next, the position (x _MIN , y _MIN ) of the pixel at which the value of n_r is the smallest is searched from among the pixels in the current random dot pattern d_r where no dot is present. Then, the dot at the position of the coordinate (x _MAX , y _MAX ) is moved to the position of the coordinate (x _MIN , y _MIN ). That is, the pixel value at the position of the coordinate (x _MAX , y _MAX ) is set to 0, and the pixel value at the position of the coordinate (x _MIN , y _MIN ) is set to 1.

In S906, a process of moving dots is performed based on the combination result of the noise map n_r and the density unevenness map t_r. Specifically, it is as follows. First, the position (x _MAX , y _MAX ) of a pixel at which the value of n_r + t_r is maximized is searched from among the pixels in the current random dot pattern d_r in which dots are present. Next, the position (x _MIN , y _MIN ) of the pixel at which the value of n_r + t_r is the smallest is searched from among the pixels in the current random dot pattern d_r where no dot exists. Then, the dot at the position of the coordinate (x _MAX , y _MAX ) is moved to the position of the coordinate (x _MIN , y _MIN ). That is, the pixel value at the position of the coordinate (x _MAX , y _MAX ) is set to 0, and the pixel value at the position of the coordinate (x _MIN , y _MIN ) is set to 1.

In S907, it is determined whether each process of S902 to S906 has been performed a predetermined number of times. The conditions such as the predetermined number of times are the same as in the first embodiment. If the number of executions has reached the predetermined number as a result of the determination, the present process is ended. Then, the random dot pattern d_r at the end of the process becomes the initial dot pattern d (g ₀ ). On the other hand, if the number of executions has not reached the predetermined number, the process returns to S902 and the process is continued.

The initial dot pattern d (g ₀ ) in the present embodiment is generated by the processing as described above.

(Effect of this embodiment)
(A) and (b) of FIG. 18 is a graph showing an example of the result of evaluating the characteristics of the threshold value matrix generated by the method of the present embodiment. The threshold matrix to be evaluated is 256 pixels in size x in the x direction and 256 pixels in size y in the y direction. Further, the gradation number g ₀ of the initial dot pattern is set to 0, and the repetitive processing of dot deletion is not performed, and only the repetitive processing of dot addition is performed. Further, using the above equation (1) as the filter coefficient k_n, and using the equation obtained by multiplying the above equation (2) by the weight W as the filter coefficient k_t, the value of σ _n is about 1.8 and the value of σ _t is The weight W was set to about 2.5 with about 8.5. Further, a value obtained by dividing the difference between the maximum value and the minimum value in the density unevenness map t (g) by the weight W is used as the density unevenness fluctuation amount v_t (g) calculated by the density unevenness fluctuation amount calculation unit 704.

  In the graph of FIG. 18A, the vertical axis represents the uneven density fluctuation amount v_t (g) calculated by the uneven density fluctuation amount calculation unit 704, the horizontal axis represents the gradation value g, and the uneven density fluctuation amount v_t (g). The larger the value, the more prominent the uneven density. The graph in FIG. 18B shows that the vertical axis represents the noise fluctuation amount v_n (g) and the horizontal axis represents the gradation value g, and the larger the value of the noise fluctuation amount v_n (g), the more noticeable the noise. There is. The difference between the maximum value and the minimum value in the noise map n (g) was used as the noise variation v_n (g).

  The graphs of FIGS. 18A and 18B show evaluation results of three types of threshold value matrices generated by changing the predetermined value th_t. Here, the predetermined value th_t is a value to be compared with the uneven concentration variation amount v_t (g) in the comparison unit 705.

  When the predetermined value th_t is ド ッ ト, since the dot arrangement is always determined taking into consideration only the noise map n (g), noise is the least noticeable as shown by the thin solid line in FIG. However, since the density unevenness map t (g) is not taken into consideration, density unevenness is most noticeable as shown by the thin solid line in FIG.

  When the predetermined value th_t is 0, a result equivalent to that of the first embodiment is obtained. That is, since the dots are arranged in consideration of both the noise map n (g) and the density unevenness map t (g), the density unevenness is the least noticeable as shown by the broken line in FIG. However, since the uneven density and the noise are in a trade-off relationship, the noise is most noticeable as indicated by the broken line in FIG.

  When the predetermined value th_t is about 2, since the method of determining the dot arrangement is switched according to the determination condition, as indicated by the thick solid line in FIG. 18A, the noise map n (g) and the density unevenness map t (g) As compared with the case where both are always considered (in the case where the predetermined value th_t is 0), uneven density is noticeable. However, as the thick solid line in FIG. 18B shows, the noise is improved. As described above, by setting the predetermined value th_t to about 2 and switching the process, the graininess can be improved as compared to the case where both the noise and the density unevenness are always considered.

  As described above, according to the present embodiment, the fluctuation amount in the density unevenness map is compared with the reference value, and in the case of less than the reference value, it is determined that the density unevenness is not noticeable, and dot arrangement is performed in consideration of noise only. As described above, by switching the method of determining the dot arrangement according to the determination condition, the graininess can be further improved as compared to the case where both noise and density unevenness are always considered.

Embodiment 3

  In the second embodiment, an aspect has been described in which the dot arrangement method is switched based on the fluctuation amount of density unevenness. Next, a mode in which the dot arrangement method is switched using the fluctuation amount of noise instead of the fluctuation amount of density unevenness will be described as a third embodiment. In addition, description is abbreviate | omitted or simplified about the part which is common in Embodiment 1 and 2, and below, it shall be demonstrated centering on difference.

  FIG. 10 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. The threshold value matrix generation unit 101 of this embodiment includes a dot pattern generation unit 701, a first filter unit 702, a second filter unit 703, a noise variation calculation unit 1001, a comparison unit 1002, a selection unit 706, and a dot arrangement unit 707. Configured FIG. 11 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. The generation process of the threshold value matrix according to the present embodiment will be described in detail below with reference to these figures. In the following description, the symbol "S" represents a step.

In S1101, the dot pattern generation unit 701 generates an initial dot pattern d (g ₀ ) having the number of gradations g = g ₀ . Here, the same dot pattern as the initial dot pattern used in the second embodiment is generated. S1102 to S1109 are processes corresponding to S802 to S809 in the second embodiment, and it is repeated starting from the initial dot pattern d (g ₀ ) generated in S1101 until the gradation value g reaches g _MAX Processing. The major difference from the second embodiment is the contents of the determination processing in S1104, and there is no difference other than that, so the description of S1102 and S1103 will be omitted.

In S1104, it is determined based on the noise map n (g) generated in S1102 whether the noise variation amount v_n (g) is smaller than a predetermined value th_n set in advance. This determination process is performed as follows by the noise variation calculation unit 1001 and the comparison unit 1002. First, the noise fluctuation amount calculation unit 1001 determines the noise fluctuation amount v_n (g). Here, v_n (g) is a scalar quantity, and in the present embodiment, the difference between extreme values in the noise map n (g) is used. In the dot addition situation, the absolute value of the difference between the minimum minimum n _MIN (g) of n (g) and the next minimum n _MIN2 (g) is used as v_n (g). Next, the comparison unit 1002 compares v_n (g) with a predetermined value th_n. The predetermined value th_n in this case is a reference value for determining whether noise is noticeable or not, and is set in advance according to the desired characteristics of the threshold value matrix. As a result of the determination, if the noise variation amount v_n (g) is smaller than the predetermined value th_n (if the determination result is true), the process proceeds to S1106, and the noise variation amount v_n (g) is larger than the predetermined value th_n (determination result (If false) proceeds to S1105.

  In S1105, dots are added to the dot pattern based on only the noise map n (g). This process is basically the same as S805 of the second embodiment, and is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S1104 is false, the selection unit 706 outputs the noise map n (g) input from the first filter unit 702. The dot placement unit 707 performs a process of adding dots to the portion with the lowest dot density, using the noise map n (g). In S1106, dots are added to the dot pattern based on the noise map n (g) and the density unevenness map t (g). This process is basically the same as S806 in the second embodiment, and is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S1104 is true, the selection unit 706 combines the noise map n (g) input from the first filter unit 702 and the density unevenness map t (g) input from the second filter unit 703, The result n (g) + t (g) is output. The dot placement unit 707 uses this n (g) + t (g) to perform a process of adding a dot to the portion with the lowest dot density.

S1107 to S1109 are the same as S807 to S809 of the second embodiment. That is, when the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MIN , y _MIN ) where the dot is added (S1107), the value of the gradation number g is incremented (S1108). Then, the process is repeated until g = g _MAX (S1109). When the tone value g = g _MAX is reached, the process proceeds to S1110.

In S1110, the initial dot pattern d (g ₀ ) having the gradation number g = g ₀ generated in S1101 is read. The subsequent S1111 to S1118 are processing of repeating deletion of dots starting from the read initial dot pattern d (g ₀ ) until the value of the gradation number g reaches g _MIN . As described above, since there is no difference from Embodiment 2 except the determination processing, the description of S1111 and S1112 will be omitted.

In S1113, it is determined based on the noise map n (g) generated in S1111 whether the noise variation amount v_n (g) is smaller than a predetermined value th_n set in advance. The basic idea of the noise variation amount v_n (g) in this step is also the same as that in the above-described S1004. The difference is that the dot remove scene, the difference between the n maximum maximal value n _MAX of (g) (g) and its next highest maximum value n _MAX2 (g), in that used as v_n (g). Next, the comparison unit 1002 compares v_n (g) with a predetermined value th_n. The predetermined value th_n here is also set in advance according to the desired characteristics of the threshold value matrix, as in S1104. As a result of the determination, if the noise variation amount v_n (g) is smaller than the predetermined value th_n (if the determination result is true), the process proceeds to S1115, and the noise variation amount v_n (g) is larger than the predetermined value th_n (determination result ) Is false) proceeds to S1114.

  In S1114, the dot pattern is deleted based on only the noise map n (g). This process is basically the same as S814 of the second embodiment, and is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S1113 is false, the selection unit 706 outputs the noise map n (g) input from the first filter unit 702. The dot placement unit 707 uses this noise map n (g) to delete dots from the portion with the highest dot density. Further, in S1115, the dot pattern is deleted based on the noise map n (g) and the density unevenness map t (g). This process is basically the same as S815 of the second embodiment, and is performed by the selection unit 706 and the dot arrangement unit 707. If the determination result in S1113 is true, the selection unit 706 combines the noise map n (g) input from the first filter unit 702 and the density unevenness map t (g) input from the second filter unit 703, The result n (g) + t (g) is output. The dot placement unit 707 uses this n (g) + t (g) to delete dots from the portion with the highest dot density.

S1116 to S1118 are the same as S816 to S818 in the second embodiment. That is, when the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MAX , y _MAX ) where the dot is deleted (S1116), the value of the gradation number g is decremented (S1117). Then, the processing until g = g _MIN is repeated (S1118). Proceeds to S1119 at the stage of reaching the gradation value g = g _MIN.

  In S1119, the range adjustment of the threshold in the threshold matrix is performed according to the range of the pixel value of the input image. This process is the same as S819 in the second embodiment.

  The above is the entire flow of the threshold value matrix generation process according to the present embodiment. In the case of the present embodiment, when the difference between extreme values in the noise map is small, it is determined that the difference in granularity due to the extreme values is small, and dots are arranged to suppress density unevenness. For example, in the case where a plurality of minimum values exist in the case where a dot is added at a position where the noise is minimum, in the second embodiment, the addition position of the dot is determined randomly. On the other hand, in the present embodiment, when the variation amount of noise is zero or smaller than a predetermined value, dots are arranged in consideration of the density unevenness map. In the present embodiment, the difference between extreme values in the noise map n (g) is used as the noise variation amount v_n (g), but the present invention is not limited to this. For example, the variance or standard deviation of n (g) may be used.

(Effect of this embodiment)
(A) and (b) of FIG. 19 is a graph showing an example of the result of evaluating the characteristics of the threshold value matrix generated by the method of the present embodiment. The threshold matrix to be evaluated is 256 pixels in size x in the x direction and 256 pixels in size y in the y direction. Further, the gradation number g ₀ of the initial dot pattern is set to 0, and the repetitive processing of dot deletion is not performed, and only the repetitive processing of dot addition is performed. Further, using the above equation (1) as the filter coefficient k_n, and using the equation obtained by multiplying the above equation (2) by the weight W as the filter coefficient k_t, the value of σ _n is about 1.8 and the value of σ _t is The weight W was set to about 2.5 with about 8.5. Further, as the noise fluctuation amount v_n (g) calculated by the noise fluctuation amount calculation unit 1001, the difference between the maximum value and the minimum value in the noise map n (g) is used.

  In the graph of FIG. 19A, the vertical axis represents the density unevenness fluctuation amount v_t (g) calculated by the density unevenness fluctuation amount calculation unit 704, the horizontal axis represents the gradation value g, and the density unevenness fluctuation amount v_t (g) The larger the value, the more prominent the uneven density. The graph in FIG. 19B shows that the vertical axis represents the noise fluctuation amount v_n (g) and the horizontal axis represents the gradation value g, and the larger the value of the noise fluctuation amount v_n (g), the more noticeable the noise. There is. The difference between the maximum value and the minimum value in the noise map n (g) was used as the noise variation v_n (g).

  The graphs of FIGS. 19A and 19B show evaluation results of three types of threshold value matrices generated by changing the value of the predetermined value th_n. The predetermined value th_n is a value to be compared with the noise fluctuation amount v_n (g) in the comparison unit 1002.

  When the predetermined value th_n is 0, the dot arrangement is always determined in consideration of only the noise map n (g), so that the noise is the least noticeable as shown by the broken line in FIG. However, since the density unevenness map t (g) is not taken into consideration, as the broken line graph in FIG. 19A shows, the density unevenness is most noticeable.

  When the predetermined value th_n is ∞, a result equivalent to that of the first embodiment is obtained. That is, since the dots are arranged in consideration of both the noise map n (g) and the density unevenness map t (g), the density unevenness is the least noticeable as shown by the thin solid line in FIG. 19 (a). However, since uneven density and noise are in a trade-off relationship, noise is most noticeable as shown by the thin solid line in FIG. 19 (b).

  When the predetermined value th_n is about 1.4, the method of determining the dot arrangement is switched according to the determination condition, so the noise map n (g) and the density unevenness map t (g (g) indicate as shown by the thick solid line in FIG. Compared to the case where both of the above are always considered (when the predetermined value th_t is ∞), uneven density is more noticeable. However, as the thick solid line in FIG. 19B shows, the noise fluctuation amount v_n (g) can be limited to about 1.4. As described above, by setting the predetermined value th_n to about 1.4 and switching the processing, concentration unevenness is suppressed while restricting the influence on graininess, as compared to the case where both noise and density unevenness are always considered. can do.

  As described above, according to the present embodiment, it is possible to suppress density unevenness while minimizing the influence on graininess.

Embodiment 4

  Next, a mode in which the dot arrangement method is switched using both the variation amount of density unevenness and the variation amount of noise will be described as a fourth embodiment. In addition, description is abbreviate | omitted or simplified about the part in common with Embodiment 1-3, and below, it shall be demonstrated centering on difference.

  FIG. 12 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. The threshold value matrix generation unit 101 according to the present embodiment includes a dot pattern generation unit 701, a first filter unit 702, a second filter unit 703, an uneven density variation amount calculation unit 704, a noise variation amount calculation unit 1001, a comparison unit 1201, a selection unit And 706 and a dot arrangement unit 707. FIG. 13 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. The generation process of the threshold value matrix according to the present embodiment will be described in detail below with reference to these figures. In the following description, the symbol "S" represents a step.

In S1301, the dot pattern generation unit 701 generates an initial dot pattern d (g ₀ ) having the number of gradations g = g ₀ . Here, the same dot pattern as the initial dot pattern used in the second embodiment is generated. The subsequent S1302 to S1309 are processing of repeating addition of dots starting from the initial dot pattern d (g ₀ ) generated in S1301 until the gradation value g reaches g _MAX . The major difference between the second embodiment and the third embodiment is the content of the determination process in S1304, and there is no difference other than that, so the description of S1302 and S1303 will be omitted.

In S1304, the noise map n (g) generated in S1302 and the density unevenness map t (g) generated in S1303 indicate whether the fluctuation amount v_t (g) of density unevenness is larger than the fluctuation amount v_n (g) of noise. And based on This determination process is performed as follows by the uneven density variation amount calculation unit 704, the noise variation amount calculation unit 1001, and the comparison unit 1201. First, the uneven density fluctuation amount calculation unit 704 and the noise fluctuation amount calculation unit 1001 respectively calculate the fluctuation amount v_t (g) of uneven density and the fluctuation amount v_n (g) of noise. For example, the difference between the maximum value and the minimum value in the density unevenness map t (g) is calculated as v_t (g), and the minimum minimum value n _MIN (g) in the noise map n (g) and its next as v_n (g) The absolute value of the difference with the small minimum value n _MIN2 (g) is calculated. Then, the calculated density unevenness fluctuation amount v_t (g) is compared with the noise fluctuation amount v_n (g), and the density unevenness fluctuation amount v_t (g) is smaller than the noise fluctuation amount v_n (g) (judgment result is false) In the case of), the process proceeds to S1305. On the other hand, if the density unevenness fluctuation amount v_t (g) is larger than the noise fluctuation amount v_n (g) (if the determination result is true), the process proceeds to S1306.

In S1305, dots are added to the dot pattern based on only the noise map n (g). That is, dots are added to the portion with the lowest dot density using only n (g). In S1306, dots are added to the dot pattern based on the noise map n (g) and the density unevenness map t (g). That is, using n (g) + t (g), dots are added to the portion with the lowest dot density. Then, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MIN , y _MIN ) where the dot is added (S1307), and the value of the gradation number g is incremented (S1308). Then, the process is repeated until g = g _MAX (S1309). When the tone value g = g _MAX is reached, the process proceeds to S1310.

In S1310, the initial dot pattern d (g ₀ ) having the gradation number g = g ₀ generated in S1301 is read. The subsequent S1311 to S1318 are processing of repeating deletion of dots from the read initial dot pattern d (g ₀ ) until the gradation value g reaches g _MIN . As described above, since there is no difference from Embodiments 2 and 3 before the determination processing, the description of S1311 and S1312 will be omitted.

In S1313, the noise map n (g) generated in S1311 and the density unevenness map t (g) generated in S1312 indicate whether the fluctuation amount v_t (g) of the density unevenness is larger than the fluctuation amount v_n (g) of the noise. And based on This determination process is performed as follows by the uneven density variation amount calculation unit 704, the noise variation amount calculation unit 1001, and the comparison unit 1201. First, the uneven density fluctuation amount calculation unit 704 and the noise fluctuation amount calculation unit 1001 respectively calculate the fluctuation amount v_t (g) of uneven density and the fluctuation amount v_n (g) of noise. For example, the difference between the maximum value and the minimum value in the density unevenness map t (g) is calculated as v_t (g), and the maximum maximum value n _MAX (g) in the noise map n (g) and the next one as v_n (g) The difference from the large maximum value n _MAX2 (g) is calculated. Then, the calculated density unevenness fluctuation amount v_t (g) is compared with the noise fluctuation amount v_n (g), and the density unevenness fluctuation amount v_t (g) is smaller than the noise fluctuation amount v_n (g) (judgment result is false) In the case of), the process proceeds to S1314. On the other hand, if the density unevenness fluctuation amount v_t (g) is larger than the noise fluctuation amount v_n (g) (if the judgment result is true), the process proceeds to S1315.

In S1314, the dot pattern is deleted based on only the noise map n (g). That is, using only n (g), dots are deleted from the portion with the highest dot density. In S1315, the dot pattern is deleted based on the noise map n (g) and the density unevenness map t (g). That is, dots are deleted from the portion with the highest dot density using n (g) + t (g). Then, the threshold value in the threshold value matrix M (x, y) is set based on the position (x _MAX , y _MAX ) from which the dot is deleted (S1316), and the value of the gradation number g is decremented (S1317). Then, the processing until g = g _MIN is repeated (S1318). Proceeds to S1319 at the stage of reaching the gradation value g = g _MIN.

  In S1319, the range adjustment of the threshold value in the threshold value matrix is performed according to the range of the pixel value of the input image. The above is the entire flow of the threshold value matrix generation process according to the present embodiment.

(Effect of this embodiment)
(A) and (b) of FIG. 20 is a graph which shows an example of the result of having evaluated the characteristic of the threshold value matrix produced | generated by the method of this embodiment. The threshold matrix to be evaluated is 256 pixels in size x in the x direction and 256 pixels in size y in the y direction. Further, the gradation number g ₀ of the initial dot pattern is set to 0, and the repetitive processing of dot deletion is not performed, and only the repetitive processing of dot addition is performed. Further, using the above equation (1) as the filter coefficient k_n, and using the equation obtained by multiplying the above equation (2) by the weight W as the filter coefficient k_t, the value of σ _n is about 1.8 and the value of σ _t is The weight W was set to about 2.5 with about 8.5. Further, a value obtained by dividing the difference between the maximum value and the minimum value in the density unevenness map t (g) by the weight W is used as the density unevenness fluctuation amount v_t (g) calculated by the density unevenness fluctuation amount calculation unit 704. Further, as the noise fluctuation amount v_n (g) calculated by the noise fluctuation amount calculation unit 1001, the difference between the maximum value and the minimum value in the noise map n (g) is used.

  In the graph of FIG. 20A, the vertical axis represents the uneven density fluctuation amount v_t (g) calculated by the uneven density fluctuation amount calculation unit 704, the horizontal axis represents the gradation value g, and the uneven density fluctuation amount v_t (g) The larger the value, the more prominent the uneven density. In the graph of FIG. 20B, the vertical axis represents the noise fluctuation amount v_n (g) calculated by the noise fluctuation amount calculation unit 1001, the horizontal axis represents the gradation value g, and the noise fluctuation amount v_n (g) is large. The noise indicates that the noise is noticeable.

  The graphs of FIGS. 20A and 20B show evaluation results of three types of threshold value matrices. The first one is an evaluation result of the threshold value matrix generated by always considering both the noise map n (g) and the density unevenness map t (g), and is shown by a broken line. The threshold matrix is generated by setting the predetermined value th_t to 0 in the method of the second embodiment. The second one is the evaluation result of the threshold value matrix generated by the method of the present embodiment, and is indicated by a thick solid line. The third one is an evaluation result of a threshold value matrix generated always in consideration of only the noise map n (g), and is shown by a thin solid line. The threshold value matrix is generated by setting the predetermined value th_t to に お い て in the method of the second embodiment.

  In the graphs of FIGS. 20A and 20B, evaluation results corresponding to the threshold value matrix of the predetermined value th_t = 0 and the threshold value matrix of the predetermined value th_t = ∞ are indicated by FIGS. 18A and 18B. As in the evaluation result, uneven density and noise are in a trade-off relationship. On the other hand, in the evaluation results of the threshold value matrix according to the method of the present embodiment, solid lines in FIGS. 20A and 20B indicate the dot arrangement by selecting the larger one of the effects of density unevenness reduction and noise reduction. As a result, both noise reduction and uneven density reduction can be achieved.

  In the case of the present embodiment, the magnitude relationship between the noise fluctuation amount and the density unevenness fluctuation amount is evaluated, and the larger one of the noise reduction and the density unevenness reduction effect is selected to determine the position of addition / deletion of dots. That is, the dots are arranged using the fluctuation amount of the larger one of the suppression effect among the fluctuation amount of density unevenness and the fluctuation amount of noise. Therefore, it is possible to achieve both noise reduction and uneven density reduction more effectively.

Embodiment 5

  In the fourth embodiment, “noise + density unevenness” can be totally improved by switching the dot arrangement method by performing determination using both the fluctuation amount of density unevenness and the fluctuation amount of noise. However, even if the total of "noise + density unevenness" is improved, there may be a case where either noise or density unevenness is deteriorated. Therefore, an embodiment will be described as a fifth embodiment in which noise and density unevenness are prevented as much as possible by determining additional positions and deletion positions of dots in consideration of the average value in the noise map and density unevenness map.

  FIG. 14 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. The threshold value matrix generation unit 101 according to the present embodiment includes a dot pattern generation unit 701, a first filter unit 702, a second filter unit 703, an uneven density variation calculation unit 704, a noise variation calculation unit 1001, a comparison unit 1401, and a dot arrangement. A section 1402 is provided. That is, in the present embodiment, the selection unit present in the fourth embodiment does not exist, and the noise map n (g) generated by the first filter unit 702, the density unevenness map t generated by the second filter unit 703 (g And the comparison result in the comparison unit 1401 is directly input to the dot arrangement unit 1402.

  FIG. 15 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. S1501 to S1504, S1507 to S1513, and S1516 to S1519 correspond to S1301 to S1304, S1307 to S1313, and S1316 to S1319 of the fourth embodiment, respectively. The process (S1505, S1506, S1514 and S1515) which considers the average value in the noise map and / or density unevenness map when determining the additional position and the deletion position of the dot, which is the feature of the present embodiment, will be described below.

In the repetitive processing of dot addition, when the density unevenness fluctuation amount v_t (g) is smaller than the noise fluctuation amount v_n (g) (No in S1504), in S1505, the dot arrangement unit 1402 first performs density unevenness map t (g) The average value tAVG (g) in Then, a dot is added at a position where the noise n (g) is the smallest among the positions where there are no dots and the density unevenness is smaller than the average value. Specifically, among the pixels whose pixel value in the dot pattern d (g) is 0, noise at the coordinates (x, y) among the pixels whose value of t (g) is smaller than tAVG (g) First, a pixel position (x _MIN , y _MIN ) at which the value of the map n (g) becomes minimum is searched. Then, the pixel value of the found pixel position (x _MIN , y _MIN ) is changed from 0 to 1. On the other hand, if the density unevenness fluctuation amount v_t (g) is larger than the noise fluctuation amount v_n (g) (Yes in S1504), the dot arrangement unit 1402 first performs the average value nAVG (N in the noise map n (g) in S1506. g) ask. Then, a dot is added at a position where the density unevenness t (g) is the smallest among the positions where the dot is not present and the noise is smaller than the average value. Specifically, among the pixels whose pixel value in the dot pattern d (g) is 0, the density at the coordinate (x, y) among the pixels whose value of n (g) is smaller than nAVG (g) First, a pixel position (x _MIN , y _MIN ) at which the value of the unevenness map t (g) becomes minimum is searched. Then, the pixel value of the found pixel position (x _MIN , y _MIN ) is changed from 0 to 1. In S1506, dots may be added at a position where the sum of n (g) and t (g) is minimized, instead of the position where the density unevenness t (g) is minimized.

In the repetitive processing of dot deletion, when the density unevenness fluctuation amount v_t (g) is smaller than the noise fluctuation amount v_n (g) (No in S1513), the dot arrangement unit 1402 first performs density unevenness map t (g) in S1514. The average value tAVG (g) in Then, the dot is deleted from the position where the noise n (g) is the largest among the positions where the dot is present and the density unevenness is larger than the average value. Specifically, among the pixels whose pixel value in the dot pattern d (g) is 1, noise at the coordinates (x, y) among the pixels whose value of t (g) is larger than tAVG (g) First, a pixel position (x _MAX , y _MAX ) at which the value of the map n (g) becomes maximum is searched. Then, the pixel value of the found pixel position (x _MAX , y _MAX ) is changed from 1 to 0. On the other hand, if the density unevenness fluctuation amount v_t (g) is larger than the noise fluctuation amount v_n (g) (Yes in S1513), the dot arrangement unit 1402 first performs the average value nAVG (Am in the noise map n (g)) in S1515. g) ask. Then, among the positions where the dots are present and the noise is larger than the average value, the dots are deleted from the position where the uneven density t (g) is the largest. Specifically, among the pixels whose pixel value in the dot pattern d (g) is 1, among the pixels whose value of n (g) is larger than nAVG (g), the density at the coordinates (x, y) First, a pixel position (x _MAX , y _MAX ) at which the value of the unevenness map t (g) becomes maximum is searched. Then, the pixel value of the found pixel position (x _MAX , y _MAX ) is changed from 1 to 0. In S1515, the dot may be deleted from the position where the sum of n (g) and t (g) is maximum, not the position where the density unevenness t (g) is maximum.

  The above is the contents of the threshold value matrix generation processing according to the present embodiment.

(Effect of this embodiment)
(A) and (b) of FIG. 21 is a graph showing an example of the result of evaluating the characteristics of the threshold value matrix generated by applying the method of the present embodiment to the method of the fourth embodiment. The threshold matrix to be evaluated is 256 pixels in size x in the x direction and 256 pixels in size y in the y direction. Further, the gradation number g ₀ of the initial dot pattern is set to 0, and the repetitive processing of dot deletion is not performed, and only the repetitive processing of dot addition is performed. Further, using the above equation (1) as the filter coefficient k_n, and using the equation obtained by multiplying the above equation (2) by the weight W as the filter coefficient k_t, the value of σn is about 1.8 and the value of σt is 8. The weight W was set to about 2.5. Further, a value obtained by dividing the difference between the maximum value and the minimum value in the density unevenness map t (g) by the weight W is used as the density unevenness fluctuation amount v_t (g) calculated by the density unevenness fluctuation amount calculation unit 704. Further, as the noise fluctuation amount v_n (g) calculated by the noise fluctuation amount calculation unit 1001, the difference between the maximum value and the minimum value in the noise map n (g) is used. Further, when the method of the present embodiment is applied to the method of the fourth embodiment, in S1305 of the flowchart of FIG. 13 described above, there are no dots and the density unevenness is smaller than the average value t _AVG (g) Of the positions, dots were added at positions where n (g) is the smallest. In S1306, dots are added at positions where the sum of n (g) and t (g) is the smallest among the positions where there is no dot and noise is smaller than the average value n _AVG (g). As described above, since the repetitive processing of dot deletion was not performed, no change was made to S1314 and S1315.

  In the graph of FIG. 21A, the vertical axis represents the density unevenness fluctuation amount v_t (g) calculated by the density unevenness fluctuation amount calculation unit 704, the horizontal axis represents the gradation value g, and the density unevenness fluctuation amount v_t (g) The larger the value, the more prominent the uneven density. In the graph of FIG. 21B, the vertical axis represents the noise fluctuation amount v_n (g) calculated by the noise fluctuation amount calculation unit 1001, the horizontal axis represents the gradation value g, and the noise fluctuation amount v_n (g) is large. The noise indicates that the noise is noticeable.

  The graphs of FIGS. 21 (a) and 21 (b) show evaluation results of two types of threshold value matrices. One is an evaluation result of the threshold value matrix generated by the method of the present embodiment, and is indicated by a thick solid line. The other is the evaluation result of the threshold value matrix generated by the method of the fourth embodiment, and is indicated by a broken line. Comparing the evaluation result of the fourth embodiment with the evaluation result of the present embodiment, it can be seen that although the noise is almost the same, the peak of the uneven density is suppressed in the present embodiment.

  According to the present embodiment, when adding a dot, if using the minimum value in the noise map, use a pixel below the average value in the density unevenness map; if using the minimum value in the density unevenness map, add pixels using an average value in the noise map The subject is limited. Similarly, when deleting dots, when using the maximum value in the noise map, the target is the pixel exceeding the average value of density unevenness, and when using the maximum value in the density unevenness map, the target is the pixel exceeding the average value in the noise map It is limited. As a result, it is possible to simultaneously prevent the deterioration of one of the noise and the density unevenness while improving it.

In addition, the determination method of the addition position of the dot mentioned above and a deletion position is applicable also to Embodiment 1-3. For example, in S404 in the case of applying to the first embodiment, the sum of n (g) and t (g) among the positions where there is no dot and noise is smaller than the average value n _AVG (g) You can add a dot at the position where is the smallest. The same applies to the other embodiments.

Embodiment 6

  Next, based on the prior art (see Patent Document 3) which generates a robust threshold value matrix for the deviation between dot patterns, both the noise reduction and the density unevenness reduction are compatible and the threshold value is robust against the deviation between dot patterns. An aspect of generating a matrix will be described as a sixth embodiment. The description of the portions common to the above-described embodiment will be omitted or simplified, and in the following, differences will be mainly described.

  FIG. 22A is a view for explaining a printing method by the ink jet method. The image forming unit 112 of the present embodiment performs printing using two nozzle rows (nozzle row A and nozzle row B) per color. In FIG. 22A, for convenience of explanation, the number of nozzles for each nozzle row is eight, but for example, in the case of a 10-line full line type of 1200 dpi, 12000 nozzles are arranged in each nozzle row. become. (B) and (c) of FIG. 22 show the positions (discharge positions of ink droplets) of dots on the recording medium, FIG. 22 (b) corresponds to the nozzle row A, and FIG. 22 (c) shows the nozzles Corresponds to column B. The nozzle row A forms even rows, and the nozzle row B forms odd rows, thereby enabling high-speed image formation. A halftoning image (halftone image) to be used for printing is subjected to dither processing in the quantization processing unit 214, and is generated for each nozzle row. In this case, a threshold value matrix used for dithering is also generated for each nozzle row.

  First, the quantization processing unit 214 equally distributes the image signal output from the γ correction processing unit 213 to the nozzle array A and the nozzle array B. Then, different threshold value matrices are respectively applied to the image signal distributed to the nozzle array A and the image signal distributed to the nozzle array B, thereby generating a halftone image used for printing. At this time, when the dot pattern of the halftone image for the nozzle array A and the dot pattern of the halftone image for the nozzle array B are superimposed, it is desirable that both noise and uneven density be good. Further, even if positional deviation occurs between dot patterns due to a mounting error of the nozzle array, a deviation of the ink ejection timing, or the like, it is desirable that the noise and the uneven density do not deteriorate.

  In this respect, in the prior art, in order to have a certain degree of correlation between the dot pattern of the nozzle row A and the dot pattern of the nozzle row B, each of the dot patterns of the two nozzle rows is taken into consideration in consideration of noise. Generate a threshold matrix of nozzle rows. Therefore, the noise when the dot patterns are superimposed is good, and the deterioration of the noise can be suppressed even if the positional deviation occurs between the dot patterns of the two nozzle rows. However, since the density unevenness is not taken into consideration, the density unevenness when the dot patterns of the two nozzle rows overlap is not always good, and the positional deviation occurs between the dot patterns of the two nozzle rows. It can not be suppressed until the deterioration of density unevenness.

  On the other hand, in the present embodiment, when generating the threshold value matrix of each nozzle row, not only the noise but also the density so that the dot pattern of the nozzle row A and the dot pattern of the nozzle row B have a certain degree of correlation. Dot arrangement is performed in consideration of unevenness. Therefore, when dot patterns are superimposed, not only noise but also density unevenness become good, and even if positional deviation occurs between dot patterns, deterioration of both noise and density unevenness can be suppressed.

  In the following description, the threshold matrix for the nozzle array A is denoted as “M_A (x, y)”, and the threshold matrix for the nozzle array B is denoted as “M_B (x, y)”. Since nozzle array A forms even rows, when generating threshold matrix M_A (x, y), the threshold is stored only in even rows satisfying y% 2 = 0, and odds satisfying y% 2 = 1 Rows do not store thresholds. On the other hand, since nozzle row B forms odd rows, when generating threshold matrix M_B (x, y), the threshold is stored only in the odd rows satisfying y% 2 = 1, and y% 2 = 0 No threshold is stored in the even row that is satisfied. Note that "%" indicates a remainder operation. Further, the size of the threshold value matrix in the x direction is “Sx”, the size in the y direction is “Sy”, and in this embodiment, Sx is 256 pixels and Sy is 256 pixels. However, Sx = 256 pixels and Sy = 256 pixels are an example, and they can be of any size. Further, a dot pattern in the process of generating the threshold matrix M_A (x, y) is denoted as “d_A (x, y)”, and a dot pattern in the process of generating the threshold matrix M_B (x, y) is represented by “d_B (x, y)”. , Y) ”. In order to form even rows, nozzle row A arranges dots only in even rows satisfying y% 2 = 0 in dot pattern d_A (x, y), and dots in odd rows satisfying y% 2 = 1 Do not place On the other hand, in order to form an odd row, nozzle row B arranges dots only in the odd row satisfying y% 2 = 1 in dot pattern d_B (x, y), and even dots satisfying y% 2 = 0 Does not place a dot.

  The dot patterns d_A (x, y) and d_B (x, y) change in an iterative process of dot addition and dot deletion. When the size Sy in the y direction of the threshold value matrix is even, from the dot pattern with 0 dots, Sx × Sy ÷ 2 + 1 dot patterns with dot numbers up to Sx × Sy ÷ 2 are included in each process of repetition. It is generated. Therefore, when the dot number in dot pattern d_A (x, y), d_B (x, y) is set to g, one particular time point in an iterative process can be specified if this g is used. In the following description, the dot patterns d_A (x, y) and d_B (x, y) when the number of dots (= number of gradations) g is simply “d_A (g)” with the coordinates x and y omitted. It will be written as "d_B (g)".

  FIG. 23 is a functional block diagram showing a software configuration for realizing the threshold value matrix generation unit 101 according to the present embodiment. The threshold value matrix generation unit 101 of this embodiment includes a dot pattern generation unit 2301, first filter units 2302A and 2302B, second filter units 2303A and 2303B, adders 2304A and 2304B, evaluation value calculation units 2305A and 2305B, and a dot arrangement unit. It consists of 2306A and 2306B.

  FIG. 24 is a flowchart showing the entire flow of threshold matrix generation processing according to the present embodiment. The generation process of the threshold value matrix according to the present embodiment will be described in detail below with reference to these figures. In the following description, the symbol "S" represents a step.

In S2401, the dot pattern generation unit 2301 generates initial dot patterns d_A (g ₀ ) and d_B (g ₀ ) having tone values of g ₀ . The gradation number g ₀ of the initial dot pattern is Sx × Sy ÷ 2 × 0.5 = 256 × 256/2 × 0.5 = 16384. Details of the method of generating the initial dot pattern according to this embodiment will be described later.

Steps S2402 to S2409 correspond to steps S402 to S407 in the first embodiment, and until the value of the gradation number g reaches g _MAX starting from the initial dot pattern d (g ₀ ) generated in S2401 It is an iterative process. In this embodiment, g _MAX ≧ g ₀ and g _MAX = Sx × Sy ÷ 2 = 32768.

  In S2402, the first filter unit 2302A applies a low pass filter having a filter coefficient k_n to the dot pattern d_A (g) corresponding to the gradation value g to extract noise components, and indicates noise distribution state Generate a map n_A (g). This process is the same as S402 of the first embodiment. In addition, the second filter unit 2303A applies a low pass filter having a filter coefficient k_t to the dot pattern d_A (g) corresponding to the gradation value g to extract the density unevenness component, and indicates the distribution state of the density unevenness. A density unevenness map t_A (g) is generated. This process is the same as S403 of the first embodiment. Further, the adding unit 2304A adds the noise map n_A (g) and the density unevenness map t_A (g) to generate a combined map S_A (g) = n_A (g) + t_A (g).

  In S 2403, the first filter unit 2302 B applies a low pass filter having a filter coefficient k_n to the dot pattern d_B (g) corresponding to the gradation value g to extract noise components and show noise distribution state Generate a map n_B (g). This process is the same as S402 of the first embodiment. In addition, the second filter unit 2303B applies a low pass filter having a filter coefficient k_t to the dot pattern d_B (g) corresponding to the gradation value g to extract the density unevenness component, and indicates the distribution state of the density unevenness. A density unevenness map t_B (g) is generated. This process is the same as S403 of the first embodiment. Further, the adding unit 2304B adds the noise map n_B (g) and the density unevenness map t_B (g) to generate a combined map S_B (g) = n_B (g) + t_B (g).

In S2404, the evaluation value calculation unit 2305A performs weighted addition of the combined map S_A (g) and the combined map S_B (g) to generate an evaluation value E_A (g) = S_A (g) + α × S_B (g). . The weight α will be described later. Then, using the E_A (g) received from the evaluation value calculation unit 2305A, the dot arrangement unit 2306A performs a process of adding a dot to the portion with the lowest dot density in the dot pattern d_A (g). Specifically, among the pixel positions (pixel value = 0) at which dots do not exist in the even rows of the dot pattern d_A (g), the evaluation value E_A (g) at the coordinates (x, y) is minimized. The pixel position (x _{MIN — A} , y _{MIN — A} ) is searched. Then, the pixel value of the found pixel position (x _{MIN — A} , y _{MIN — A} ) is changed from 0 to 1. When a plurality of pixel positions at which the value of E_A (g) is the smallest are found, the position to which a dot is added may be randomly selected from among them.

In S2405, the threshold value in the threshold value matrix M_A (x, y) is set based on the position ( _{xMIN_A} , _{yMIN_A} ) to which the dot is added. Specifically, the threshold matrix M_A (x, y) M_A in (x _{MIN_A,} y _{MIN_A)} as the value of the current value of g is set.

In S2406, the evaluation value calculation unit 2305B performs weighted addition of the combined map S_B (g) and the combined map S_A (g) to generate an evaluation value E_B (g) = S_B (g) + α × S_A (g). . The weight α will be described later. Then, using the E_B (g) received from the evaluation value calculation unit 2305B, the dot arrangement unit 2306B performs a process of adding dots to the portion with the lowest dot density in the dot pattern d_B (g). Specifically, among the pixel positions (pixel value = 0) at which dots do not exist in the odd rows of the dot pattern d_B (g), the evaluation value E_B (g) at the coordinates (x, y) is minimized. The pixel position (x _{MIN — B} , y _{MIN — B} ) is searched. Then, the pixel value of the found pixel position (x _{MIN — B} , y _{MIN — B} ) is changed from 0 to 1. When a plurality of pixel positions at which the value of E_B (g) is minimum are found, the position to which a dot is added may be randomly selected from among them.

In S2407, the threshold value in the threshold value matrix M_B (x, y) is set based on the position ( _{xMIN_B} , _{yMIN_B} ) to which the dot is added. Specifically, the threshold matrix M_B (x, y) M_B in (x _{MIN_A,} y _{MIN_A)} as the value of the current value of g is set.

In the subsequent S2408, the value of the gradation number g is incremented, and g = g + 1. Then, in S2409, it is determined whether the value of the gradation number g has reached g _MAX (here, 32768). If it is not g = g _{MAX as} a result of a judgment, it will return to S2402 and processing will be continued. On the other hand, the process proceeds to S2410 if g = g _MAX. These processes are the same as S406 and S407 in the first embodiment.

In S2410, the initial dot patterns d_A (g ₀ ) and d_B (g ₀ ) in which the gradation value generated in S2401 is g ₀ = 16384 are read. The subsequent S2411 to S2418 are processes corresponding to S409 to S414 in the first embodiment, and the value of the gradation number g reaches g _MIN starting from the initial dot pattern d (g ₀ ) generated in S2401. The process is repeated up to Also in the present embodiment, g _MIN ≦ g ₀ and g _MIN = 0. In the following, the iterative process of dot deletion will be described focusing on the difference from the iterative process of dot addition.

  S2411 is the same as S2402, and the first filter unit 2302A, the second filter unit 2303A, and the addition unit 2304A generate a combined map S_A (g) = n_A (g) + t_A (g). The subsequent S2412 is the same as S2403, and the first filter unit 2302B, the second filter unit 2303B, and the addition unit 2304B generate a combined map S_B (g) = n_B (g) + t_B (g).

In S2413, as in S2404, the evaluation value calculation unit 2305A generates an evaluation value E_A (g) = S_A (g) + α × S_B (g). Then, using the E_A (g) received from the evaluation value calculation unit 2305A, the dot arrangement unit 2306A performs a process of deleting dots from the portion with the highest dot density in the dot pattern d_A (g). Specifically, among the pixel positions (pixel value = 1) at which dots exist in the even rows of the dot pattern d_A (g), the evaluation value E_A (g) at the coordinates (x, y) is maximized. The pixel position (x _{MAX_A} , y _{MAX_A} ) is searched. Then, the pixel value of the found pixel position ( _{xMAX_A} , _{yMAX_A} ) is changed from 1 to 0. When a plurality of pixel positions at which the value of E_A (g) is maximum are found, the positions at which dots are to be deleted may be randomly selected from among them.

In S2414, the threshold value in the threshold value matrix M_A (x, y) is set based on the position ( _{xMIN_A} , _{yMIN_A} ) from which the dot is deleted. Specifically, the threshold matrix M_A (x, y) M_A in (x _{MIN_A,} y _{MIN_A)} as the value of the current value of g is set.

In S2415, as in S2406, the evaluation value calculation unit 2305B generates an evaluation value E_B (g) = S_B (g) + α × S_A (g). Then, using the E_B (g) received from the evaluation value calculation unit 2305B, the dot arrangement unit 2306B performs a process of deleting dots from the portion with the highest dot density in the dot pattern d_B (g). Specifically, among the pixel positions (pixel value = 1) at which dots exist in the odd rows of the dot pattern d_B (g), the evaluation value E_B (g) at the coordinates (x, y) is maximized. The pixel position (x _{MAX_B} , y _{MAX_B} ) is searched. Then, the pixel value of the found pixel position (x _{MAX — B} , y _{MAX — B} ) is changed from 1 to 0. When a plurality of pixel positions at which the value of E_B (g) is maximum are found, the positions at which dots are to be deleted may be randomly selected from among them.

In S2416, the threshold value in the threshold value matrix M_B (x, y) is set based on the position ( _{xMIN_B} , _{yMIN_B} ) at which the dot is deleted. Specifically, the current value of g is set as the value of M_B (x _{MIN _ B} , y _{MIN _} B) in the threshold value matrix M _ B (x, y).

In the subsequent S2417, the value of the number of gradations g is decremented to g = g-1. Then, in S2418, it is determined whether the value of the number of gradations g has reached g _MIN (here, 0). If it is not g = g _{MIN as} a result of determination, it will return to S2411 and processing will be continued. On the other hand, the process proceeds to S2419 if g = g _MIN. These processes are the same as S413 and S414 of the first embodiment.

In S2419, the range adjustment of the threshold value in the threshold value matrix is performed according to the range of the pixel value of the input image. At the stage where the processing has advanced to this step, values of g _MIN to g _MAX (0 to 32768 in the present embodiment) are stored in the threshold matrices M_A (x, y) and M_B (x, y). ing. Assuming that the range of the γ-corrected image input to the quantization processing unit 214 is, for example, 0 to 255, the range of signals equally distributed to the nozzle row A and the nozzle row B is 0 to 127. Even if it applies the threshold value matrix in which the value of 0-32768 was stored with respect to the image of this 0-127 range, an appropriate quantization result can not be obtained. Therefore, in this step, the same process as S415 of the first embodiment is applied to the threshold value matrices M_A (x, y) and M_B (x, y) to adjust the range of values of the threshold value matrix.

  The above is the entire flow of the threshold value matrix generation process according to the present embodiment.

(For weight α)
The weight α used in the evaluation value calculation units 2305A and 2305B is a parameter for controlling the correlation between the dot pattern d_A (g) of the nozzle array A and the dot pattern d_B (g) of the nozzle array B.

  In the case of α = 0, only the composite map of one nozzle row is reflected in the evaluation value, and the composite map of the other nozzle row is not reflected in the evaluation value, so the dot pattern d_A (g) and the dot pattern d_B ( g) is uncorrelated. When the dot pattern d_A (g) and the dot pattern d_B (g) are uncorrelated, the deterioration of noise and uneven density due to positional deviation between dot patterns is minimized.

  In the case of α = 1, the composite map of both nozzle rows is equally reflected in the evaluation value. In this case, the arrangement of the dot patterns superposed in a non-displaced state is optimized, so that noise and density unevenness become least noticeable when there is no positional deviation of the dot patterns. However, since the dot pattern d_A (g) and the dot pattern d_B (g) have a correlation, the deterioration of the noise and the uneven density due to the positional deviation between the dot patterns becomes greater than in the case of α = 0.

  In the case of 0 <α <1, the correlation between the dot pattern d_A (g) and the dot pattern d_B (g) becomes weaker than in the case of α = 1. Is smaller than in the case of α = 1. On the other hand, since the correlation between d_A (g) and d_B (g) is stronger than in the case of α = 0, noise and uneven density become less noticeable when there is no positional deviation of the dot pattern.

  In the present embodiment, the value of α is set to 0.6.

(How to generate initial dot pattern)
The method of generating the initial dot pattern according to the present embodiment will be described in detail with reference to the flowchart of FIG. In the following description, the symbol "S" represents a step.

First, in S2501, a random dot pattern d_A_r gradation depth g = g _0, generates a D_B_r. Since d_A_r corresponds to the nozzle array A, dots are arranged only in even rows. Since d_B_r corresponds to the nozzle row B, dots are arranged only in odd rows.

Specifically, from the image having no dot at first, dots are repeatedly added until the number of gradations reaches g ₀ (in the present embodiment, g ₀ = 16384) to generate d_A_r. Next, d_B_r is generated by performing the same process. The candidate of the position to add a dot generates a random number of 0 to Sx, for example, to determine the position x_r in the x direction, and similarly generates a random number of 0 to Sy to determine the position yr in the y direction. When d_A_r is generated, when the position of coordinates (x_r, y_r) determined by the random number is an even line and there is no dot, a dot is added to the position (the pixel value is changed from 0 to 1). When the pixel position is an odd row, or when the pixel value of the pixel position is already 1, a random number is newly generated to change a candidate of a position to which a dot is added. Such processing is repeated to obtain a random dot pattern d_A_r. In addition, when generating d_B_r, when the position of coordinates (x_r, y_r) determined by the random number is an odd line and there is no dot, add a dot to the position (change the pixel value from 0 to 1) Do. When the pixel position is an even row, or when the pixel value of the pixel position is already 1, a random number is newly generated to change the candidate of the position to which a dot is added. The above process is repeated to obtain a random dot pattern d_B_r.

Then, at S2502, against random dot pattern d_A_r number of gradations g ₀ generated in S2501, applying the same processing as S2402 described above, to produce a composite map S_A_r. In the subsequent S2503, with respect to the random dot pattern d_B_r number of gradations g ₀ generated in S2501, to apply the same process as S2403 described above, to produce a composite map S_B_r.

Then, in S2504, the same process as S2404 described above is applied to obtain an evaluation value E_A_r, and based on this E_A_r, a process of moving a dot is performed. First, in the even rows of the current random dot pattern d_A_r, the position ( _{xMAX_A} , _{yMAX_A} ) of the pixel at which the value of the evaluation value E_A_r is maximized is searched among the pixels in which the dot is present. Next, in the even rows of the current random dot pattern d_A_r, the position ( _{xMIN_A} , _{yMIN_A} ) of the pixel at which the value of the evaluation value E_A_r is the smallest is searched among the pixels without dots. Then, the coordinates (x _{MAX_A,} y _{MAX_A)} dots at the position of coordinates (x _{MIN_A,} y _{MIN_A)} is moved to a position. That is, the pixel value at the position of the coordinate ( _{xMAX_A} , _{yMAX_A} ) is set to 0, and the pixel value at the position of the coordinate ( _{xMIN_A} , _{yMIN_A} ) is set to 1.
Similarly, in S2505, the same process as S2406 described above is applied to obtain an evaluation value E_B_r, and based on this E_B_r, a process of moving a dot is performed. First, in the odd row of the current random dot pattern d_B_r, the position ( _{xMAX_B} , _{yMAX_B} ) of the pixel at which the value of the evaluation value E_B_r is maximized is searched among the pixels in which the dot exists. Next, in the odd rows of the current random dot pattern d_B_r, the position (x _{MIN — B} , y _{MIN — B} ) of the pixel at which the value of the evaluation value E_B_r is the smallest is searched among the pixels without dots. Then, the coordinates (x _{MAX_B,} y _{MAX_B)} dots at the position of coordinates (x _{MIN_B,} y _{MIN_B)} is moved to a position. That is, the pixel value at the position of the coordinate ( _{xMAX_B} , _{yMAX_B} ) is set to 0, and the pixel value at the position of the coordinate ( _{xMIN_B} , _{yMIN_B} ) is set to 1.

In S2506, it is determined whether or not each process from S2502 to S2505 has been performed a predetermined number of times. If the number of executions has reached the predetermined number as a result of the determination, the present process is ended. Then, the random dot patterns d_A_r and d_B_r at the end of the process become the initial dot patterns d_A (g ₀ ) and d_B (g ₀ ), respectively. On the other hand, if the number of executions has not reached the predetermined number, the process returns to S2502 and the process is continued.

The initial dot patterns d_A (g ₀ ) and d_B (g ₀ ) in the present embodiment are generated by the processing as described above.

(Effect of this embodiment)
FIG. 26 to FIG. 28 are graphs showing an example of the result of evaluating the characteristics of the threshold value matrix generated according to this embodiment. The threshold matrix to be evaluated is 256 pixels in size x in the x direction and 256 pixels in size y in the y direction. Further, the gradation number g0 of the initial dot pattern is set to 0, and the repetitive processing of dot deletion is not performed, and only the repetitive processing of dot addition is performed. Further, using the above equation (1) as the filter coefficient k_n, and using the equation obtained by multiplying the above equation (2) by the weight W as the filter coefficient k_t, the value of σn is about 1.8 and the value of σt is 8. The weight W was set to about 2.5. In the following description, a dot pattern in which the dot pattern of the nozzle row A and the dot pattern of the nozzle row B are superimposed in a state where there is no positional displacement is referred to as a "misalignment-free composite dot pattern". Further, a dot pattern obtained by superposing the dot pattern of the nozzle array A and the dot pattern of the nozzle array B in a state in which positional deviation occurs is referred to as a “synthesized combined dot pattern”. Further, as described above, the range of the gradation value g is 0 to 32768 in one nozzle row, but in FIGS. 26 to 28, in the dot pattern obtained by combining the dot patterns of both nozzle rows Since the evaluation value is shown, the range of the gradation value g on the horizontal axis of the graph is from 0 to 65,536.

  In the graph of FIG. 26A, the vertical axis represents the density non-uniformity fluctuation amount v_t (g) of the non-displaced composite dot pattern, the horizontal axis represents the gradation value g, and the value of the density non-uniformity fluctuation amount v_t (g) increases. It shows that the density unevenness of the synthetic dot pattern without deviation is noticeable. Here, the density unevenness fluctuation amount v_t (g) is obtained by calculating the density unevenness map t (g) by the cyclic convolution operation of the synthetic dot pattern without deviation and the filter coefficient k_t, and the maximum value and the minimum value of this t (g) Is the value obtained by dividing by the weight W. In the graph of FIG. 26 (b), the vertical axis represents the noise fluctuation amount v_n (g) of the synthetic dot pattern without displacement, and the horizontal axis represents the gradation value g. The larger the noise fluctuation amount v_n (g), the more noise It shows that it stands out. The noise fluctuation amount v_n (g) here is a difference between the maximum value and the minimum value of the noise map n (g) obtained by the cyclic convolution operation of the non-displacement synthesized dot pattern and the filter coefficient k_n.

  The graphs of FIGS. 26 (a) and 26 (b) show evaluation results of two types of threshold value matrices. One is an evaluation result of the threshold value matrix generated by the method of the present embodiment, and is indicated by a thick solid line. The other is an evaluation result of a threshold value matrix generated according to the prior art described in Patent Document 3 and not considering density unevenness but considering noise, which is indicated by a broken line. In each case, the value of α is 0.6. When the evaluation result of the present embodiment and the evaluation result of the prior art are compared, it is understood that although the noise is slightly deteriorated in the threshold value matrix generated according to the present embodiment, the uneven density is significantly improved.

  The graphs in FIG. 27 and FIG. 28 show the evaluation result (thick solid line) of the synthetic dot pattern without shift and the evaluation result (dotted line) of the synthetic dot pattern with shift for the threshold value matrix generated by the method of the present embodiment. It is done. In this case, both graphs in FIGS. 27A and 27B are evaluation results of the threshold value matrix generated by setting the value of α to 0.6, and the graphs in FIGS. 28A and 28B. Is the evaluation result of the threshold value matrix generated by setting the value of α to 1.0. Further, the synthetic dot pattern with shift is a dot pattern in which the dot pattern of the nozzle row B is overlapped with the dot pattern of the nozzle row A by shifting one pixel in the x direction and one pixel in the y direction.

When the value of α is set to 0.6, the noise is a little worse due to the positional deviation as shown in the graph of FIG. 27 (b). However, since the correlation between dot patterns is weaker than when α is set to 1.0 (see FIG. 28B), even if positional deviation occurs as shown in the graph of FIG. The uneven density hardly deteriorates. On the other hand, when the value of α is set to 1.0, the correlation between the dot patterns becomes strong, so as shown in the graphs of FIG. Getting worse. However, in the case where there is no positional deviation, noise and density unevenness are slightly improved, as compared with the case where the value of α is set to 0.6.
As described above, in the case of the method of the present embodiment, when the dot pattern of the nozzle row A and the dot pattern of the nozzle row B are superimposed, not only noise but also uneven density becomes good. In addition, it can be understood that by setting the value of α to a value of about 0.6, it is possible to suppress the deterioration of noise and uneven density even if deviations between dot patterns occur.

(Modification)
In addition, synthetic | combination map S_A (g) used for calculation of evaluation value E_B (g) by S2406 is calculated by S2402. However, since the state of the dot pattern d_A (g) changes in S2404 before S2406, the combined map S_A (g) calculated in S2402 is used for calculating the evaluation value E_B (g) in S2406. And, there is a possibility that the dot can not be added at the optimal position. Therefore, S2402 may be executed immediately before S2406 to recalculate the combined map S_A (g). Similarly in the process of deleting dots from S2410 to S2418, S2411 may be executed immediately before S2415 to recalculate the combined map S_A (g). The same applies to the process of generating the initial dot pattern, and S2502 may be executed immediately before S2505 to recalculate the combined map S_A_r.

  Further, in the present embodiment, only the even numbered rows are printed in the nozzle row A, and only the odd numbered rows are printed in the nozzle row B. However, such restriction of the dot arrangement is not essential. For example, printing may be performed in a staggered pattern by the nozzle array A, and printing may be performed in a reverse zigzag pattern by the nozzle array B. Alternatively, the even number rows satisfying x% 2 = 0 may be printed by the nozzle row A, and the odd number rows satisfying x% 2 = 1 may be printed by the nozzle row B. The threshold matrixes corresponding to these can be easily generated only by changing the dot addition constraints such that dots can be added to even rows only or dots can be added only to odd rows in S2404 and S2406.

  Further, the present embodiment can also be applied to the case where the number of nozzle rows is three or more. For example, when there are three nozzle rows and the nozzle row C exists in addition to the nozzle row A and the nozzle row B, a row in which the nozzle row A satisfies y% 3 = 0 is printed, and the nozzle row B is y% 3 Print a row satisfying = 1 and a row satisfying a nozzle column C satisfying y% 3 = 2. In this case, in the flow of FIG. 24, a step of generating a composite map S_C (g) of the nozzle array C, and a step of adding a dot at a position where the evaluation value E_C (g) is obtained and the E_C (g) becomes minimum. And the step of setting the value of the threshold matrix M_C (g) may be added. The method of generating the composite map S_C (g) is the same as S2402 and S2403. The method for obtaining the evaluation value E_C (g) is the same as in S2404 and S2406, and the combined map of each nozzle row is weighted and added. At this time, the weight of the focused nozzle row is set to 1, and the weights of the other two nozzle rows are set to α. That is, evaluation value E_C (g) = S_C (g) + α × S_A (g) + α × S_B (g). Similarly, in S2404, evaluation value E_A (g) = S_A (g) + α × S_B (g) + α × S_C (g), and in S2406, evaluation value E_B (g) = S_B (g) + α × S_A (g) ) + Α × S_C (g). The method of setting the value of the threshold value matrix M_C (g) is the same as S2405 and S2407. By applying such a change also to the process of deleting dots from S2410 to S2418, the present embodiment can be applied even when the number of nozzle rows is three or more.

  Further, the present embodiment can also be applied to the case of printing color materials of different colors. For example, when the colorants are four colors of C, M, Y, and K, the present embodiment is applied assuming that the nozzle rows are four rows. Thereby, the correlation of the dot pattern can be controlled between colors.

  Also in the present embodiment, as in the second to fifth embodiments, only the noise and the processing performed in consideration of both the noise and the density unevenness are considered based on the noise fluctuation amount and the density unevenness fluctuation amount. The process to be performed may be switched. When only noise is considered, the evaluation value E_A (g) in S2404 and S2413 is n_A (g) + α × n_B (g), and the evaluation value E_B (g) in S2406 and S2415 is n_B (g) + α × It may be n_A (g).

(Other embodiments)
The present invention supplies a program that implements one or more functions of the above-described embodiments to a system or apparatus via a network or storage medium, and one or more processors in a computer of the system or apparatus read and execute the program. Can also be realized. It can also be implemented by a circuit (eg, an ASIC) that implements one or more functions.

301 dot pattern generation unit 302 first filter unit 303 second filter unit 304 addition unit 305 dot arrangement unit

Claims (14)

A method of generating a threshold matrix used for quantization processing, comprising:
A first filtering step of applying a first filter to a dot pattern of a certain gradation to generate information indicative of a noise distribution state;
A second filtering step of applying a second filter to the dot pattern of a certain gradation to generate information indicating a distribution state of density unevenness;
A dot pattern generation step of generating a dot pattern of gradation adjacent to the certain gradation based on the information indicating the distribution state of the noise and the information indicating the distribution state of the density unevenness;
Values of the threshold matrix based on dot patterns of all gradations obtained by repeatedly applying the first filtering step, the second filtering step and the dot pattern generation step starting from a dot pattern of a predetermined gradation Decision process to determine
A method of generating a threshold matrix, comprising: In the dot pattern generation process,
Generating a weighted sum of the evaluation value of noise and the evaluation value of density unevenness from the information indicating the distribution state of the noise and the information indicating the distribution state of the density unevenness;
At least one of adding dots and removing dots;
When adding the dots, dots are added at positions where the dots do not exist and the weight sum is minimum,
And deleting the dot from the position where the dot exists and the weight sum is maximum, when the dot deletion is performed.
The method according to claim 1, further comprising: A method of generating a threshold matrix used for quantization processing, comprising:
A first filtering step of applying a first filter to a dot pattern of a certain gradation to generate information indicative of a noise distribution state;
A second filtering step of applying a second filter to the dot pattern of a certain gradation to generate information indicating a distribution state of density unevenness;
A dot pattern of gradation adjacent to the certain gradation by performing addition or deletion of dots to the dot pattern based on the information indicating the distribution state of the noise and the information indicating the distribution state of the density unevenness A dot pattern generation step of generating
Values of the threshold matrix based on dot patterns of all gradations obtained by repeatedly applying the first filtering step, the second filtering step and the dot pattern generation step starting from a dot pattern of a predetermined gradation Decision process to determine
Have
In the dot pattern generation process,
A fluctuation amount calculating step of calculating a fluctuation amount of an evaluation value regarding at least one of the information indicating the distribution state of the noise and the information indicating the distribution state of the density unevenness;
A first dot pattern updating step of adding or deleting the dots using the information indicating the distribution state of the noise;
A second dot pattern updating step of adding and deleting the dots using both the information indicating the noise distribution state and the information indicating the density unevenness distribution state;
A method of generating a threshold value matrix, comprising switching between the first dot pattern updating step and the second dot pattern updating step according to the amount of fluctuation of the evaluation value. In the dot pattern generation process,
When the fluctuation amount of the evaluation value of the density unevenness is smaller than a predetermined reference value, the dots are added or deleted using the first dot pattern update process,
The second dot pattern update process is used to add or delete the dots when the fluctuation amount of the evaluation value of the density unevenness is equal to or more than the predetermined reference value. Method of generating threshold matrix. In the dot pattern generation process,
When the variation of the evaluation value of the density unevenness is less than the variation of the evaluation value of the noise, the dot is added or deleted using the information indicating the distribution state of the noise,
When the fluctuation amount of the evaluation value of the density unevenness is larger than the fluctuation amount of the evaluation value of the noise, the addition of the dots is performed using the information indicating the distribution state of the noise and the information indicating the distribution state of the density unevenness. 4. The method of generating a threshold matrix according to claim 3, wherein the deletion is performed. The method of generating a threshold value matrix according to any one of claims 3 to 5, wherein the fluctuation amount of the evaluation value is a difference between a maximum value and a minimum value in the evaluation value.   The method of generating a threshold value matrix according to any one of claims 3 to 5, wherein the fluctuation amount of the evaluation value is a difference between extreme values of the evaluation value. The fluctuation amount of the evaluation value of the density unevenness is a difference between the maximum value and the minimum value in the evaluation value,
The method of generating a threshold value matrix according to any one of claims 3 to 5, wherein the fluctuation amount of the evaluation value of the noise is a difference between extreme values of the evaluation value. In the case of adding the dots,
When the variation amount of the evaluation value of the density unevenness is smaller than the predetermined reference value, a dot is added and a dot is added at a position where the evaluation value of noise is minimum,
When the fluctuation amount of the evaluation value of the density unevenness is equal to or more than the predetermined reference value, the dot is not added and a dot is added at a position where the weighted sum of the evaluation value of noise and the evaluation value of density unevenness is minimum. And
When deleting the dots,
When the fluctuation amount of the evaluation value of the density unevenness is smaller than the predetermined reference value, the dot is present, and the dot is deleted from the position where the evaluation value of the noise is maximum.
When the fluctuation amount of the evaluation value of density unevenness is equal to or more than the predetermined reference value, the dot is present, and the dot is deleted from the position where the weighted sum of the evaluation value of noise and the evaluation value of density unevenness is maximum. The method of generating a threshold value matrix according to claim 3 or 4, characterized in that: In the case of adding the dots,
When the variation of the evaluation value of density unevenness is less than the variation of the evaluation value of noise, a dot is added and a dot is added at a position where the evaluation value of noise is minimum,
When the variation of the evaluation value of the density unevenness is larger than the variation of the evaluation value of the noise, there is no dot, and the weighted sum of the evaluation value of the noise and the evaluation value of the density unevenness is minimum. Add a dot to
When deleting the dots,
When the variation of the evaluation value of density unevenness is less than the variation of the evaluation value of noise, the dot is present, and the dot is deleted from the position where the evaluation value of noise is maximum,
When the fluctuation amount of the evaluation value of the density unevenness is larger than the fluctuation amount of the evaluation value of the noise, from the position where a dot exists and the weighted sum of the evaluation value of noise and the evaluation value of density unevenness is maximum The method of generating a threshold value matrix according to claim 3 or 5, wherein dots are deleted. In the case of adding the dots,
When the fluctuation amount of the evaluation value of the density unevenness is less than the fluctuation amount of the evaluation value of the noise, the dot does not exist, and the evaluation value of the noise among the positions where the evaluation value of the density unevenness is smaller than the average value Add a dot at the minimum position,
When the fluctuation amount of the evaluation value of the density unevenness is larger than the fluctuation amount of the evaluation value of the noise, the dot does not exist, and the noise evaluation value among the positions where the noise evaluation value is smaller than the average value Add a dot at the position where the weight sum with the evaluation value of density unevenness is minimum,
When deleting the dots,
When the fluctuation amount of the evaluation value of the density unevenness is less than the fluctuation amount of the evaluation value of the noise, the dot is present, and the evaluation value of the noise is maximum at the position where the evaluation value of the density unevenness is larger than the average value. Remove the dots from the position
When the fluctuation amount of the evaluation value of the density unevenness is larger than the fluctuation amount of the evaluation value of the noise, a dot exists and the noise evaluation value and the density among the positions where the noise evaluation value is larger than the average value The method of generating a threshold value matrix according to claim 3 or 5, wherein a dot is deleted from a position where the weight sum with the evaluation value of unevenness is maximum. Quantizing means for performing quantization processing to convert an input image having a predetermined gradation into an output image having a gradation lower than the predetermined gradation;
An image forming unit that forms the output image on a recording medium using a recording head having a plurality of recording element arrays;
In an image forming apparatus provided with
The quantization process is a quantization process by dithering using a threshold value matrix generated by the method of generating a threshold value matrix according to any one of claims 1 to 11,
An image forming apparatus characterized by And a generation unit configured to generate a threshold matrix corresponding to each printing element array in consideration of the correlation of dot patterns in the threshold matrix for each of the plurality of printing element arrays,
The image forming apparatus according to claim 12, wherein the quantization unit performs quantization processing by the dither method using a threshold value matrix corresponding to each of the recording element arrays generated by the generation unit.   A program for causing a computer to execute the method of generating a threshold matrix according to any one of claims 1 to 11.

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