JP2006345384A - Image processing method and image processing apparatus - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To prevent a printing quality level from deteriorating by preventing blur or an isolated point from being generated at a boundary in character or graphic data in an error spread method. <P>SOLUTION: The error spread method inspects the characteristics of pixel data of pixels to be processed, and pixels of a destination for which error data are spread. If there is a difference in the characteristics between the pixel data, the error data to be spread are not spread into a region having different characteristic, but spread into a region having the same characteristic to execute error spread processing. If the pixel data have the same characteristic, error spread processing is normally executed. This control prevents error spread into the region having different characteristic, thereby preventing the generation of blur or the isolated point in character data or a line image and improving the printing quality level. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an image processing method and an image forming apparatus capable of preventing generation of isolated points and obtaining good print quality in printing using an error diffusion method.

  Recently, ink jet printers have become widespread. This inkjet printer holds a plurality of colors of ink for color printing. By overprinting the plurality of colors of ink while adjusting the discharge amount, a color image can be printed while expressing many colors. The plurality of colors mainly include color materials of yellow (Y), magenta (M), and cyan (C), which are the three primary colors of additive color mixing. There are also many printers equipped with black (Bk) for printing ink amount and printing black characters clearly.

  As an image processing method in such an ink jet printer, there is a method called an error diffusion method.

  A specific example of the error diffusion method will be shown.

Assume that the corresponding pixel of the image processing is (k, m), and the image data of the corresponding pixel is a (k, m). When this image data is 8-bit data,
0 ≦ a (k, m) ≦ FF (1)
It is. Here, the image data is expressed in hexadecimal.

In the image processing, if the image data of the corresponding pixel is 0 ≦ a (k, m) <M, no hit is made.

If M≤a (k, m) ≤FF, hit.
Control that. Here, M takes a value from 0 to FF.

further,
When 0 ≦ a (k, m) <M, [M] = 0
When M ≦ a (k, m) ≦ FF, [M] = FF
And Then, an error occurs between the image data and what is actually expressed as print data. If this error is e (k, m), e (k, m) is expressed as follows.

e (k, m) = a (k, m)-[M] (2)
This e (k, m) is distributed at a predetermined ratio to surrounding pixels.

At this time, as shown in FIG. 1, it is assumed that the pixels are distributed to peripheral pixels (k, m + 1), (k + 1, m−1), (k + 1, m), and (k + 1, m + 1). If the ratio at that time is α (k, m + 1), α (k + 1, m−1), α (k + 1, m), α (k + 1, m + 1), the image data of (k, m + 1) is the original image. For data a (k, m + 1): a (k, m + 1) + e (k, m) × α (k, m + 1) (3)
And add. In this manner, the process of adding the error value to the peripheral pixels at a predetermined ratio is also performed for (k + 1, m−1), (k + 1, m), and (k + 1, m + 1). This process is sequentially performed on the target pixel. The sum of the diffusion ratios is Σα = 1 (4)
As a result, image processing can be performed in a state where the density is maintained. FIG. 2 shows the general calculation.

The ratio of previously distributing the error data is α, which is called a weighting coefficient. A well-known weighting factor is called “Floyd & Steinberg type”. (Fig. 3) There is also a so-called "Jarvis, Judice & Ninke type" in which the error diffusion area is expanded to obtain a smoother image. (Fig. 4)
In FIG. 3 and FIG. 4, each area is a pixel, and pixels in the hatched area are data to be processed. The other area in which numbers are described is a pixel to which error data is diffused, and error data e (i, j) is distributed according to the ratio of the numbers. In addition to the above methods, various diffusion methods have been proposed, and the distribution rate and distribution pixels are the know-how of the error diffusion method.

  The error diffusion method described above can reproduce smooth density conversion and can perform smooth printing. This is a very effective image processing method particularly when a printed matter such as a photographic image is generated. For this reason, the error diffusion method is currently used in many products.

Moreover, as a prior art example, patent document 1 and patent document 2 can be mention | raise | lifted, for example.
Japanese Patent Registration No. 2702593 Japanese Patent Laid-Open No. 9-219791

  However, in this error diffusion method, fine lines may be blurred in character data, line drawings, etc., or the outline may be blurred or isolated points may be generated at the boundaries. This is because the error diffusion method performs an operation of arranging image data at a place where it originally exists at another place, and therefore, surrounding pixel information is added at the time of printing.

  To eliminate this drawback, there are methods such as changing the image processing method settings for character data. However, when images and characters are mixed in the print data, appropriate settings cannot be made, which is good. Cannot be printed properly. In particular, there is a problem that character data in the image data is processed in the same manner as the image, resulting in blurred printing.

  In order to solve this problem, the image processing method according to the present invention has the following functions.

The invention of claim 1 is an image processing method using an error diffusion method,
Determine the characteristics of the source and destination image data,
Based on the result of the determination, the error data in the error diffusion method is diffused.
An image processing method characterized by this.

  As described above, the error diffusion method can be determined by determining the characteristics of the image data.

According to a second aspect of the present invention, in the determination method,
Holds information for determining the characteristics of image data or attribute data or image data for use in determination during image processing.
An image processing method characterized by this.

  In this way, by retaining the characteristics of the image data, it can be referred to during image processing.

According to a third aspect of the present invention, in the determination method,
Calculate the difference in image data between pixels,
Determine the difference,
An image processing method characterized by this.

  Thus, by calculating the difference between the image data, it is possible to determine a change in the color tone of the image data.

According to a fourth aspect of the present invention, in the determination method,
Calculate the difference in image data between pixels,
Judging by comparing the difference with a certain threshold,
An image processing method characterized by this.

  In this way, by comparing the difference of the image data with a certain threshold value, it is possible to detect a point where the change in color tone of the image data is large.

According to a fifth aspect of the present invention, in the determination method,
Judging from the attribute data included in the pixel image data,
An image processing method characterized by this.

  In this way, the characteristics of the image can be determined by referring to the attribute data.

The invention of claim 6 is a method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to diffuse the error data,
An image processing method characterized by this.

  In this way, by diffusing error data in the region having the same characteristic, it is possible to control to retain the image density and not diffuse to the region having a different characteristic.

The invention of claim 7 is a method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Diffusion data is diffused to the nearest area among the areas judged to diffuse error data.
An image processing method characterized by this.

  In this way, by diffusing error data in the region having the same characteristic, it is possible to control to retain the image density and not diffuse to the region having a different characteristic.

The invention of claim 8 is a method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
The diffusion data is diffused to the unprocessed area among the areas determined to diffuse the error data.
An image processing method characterized by this.

  In this way, by diffusing error data in the region having the same characteristic, it is possible to control to retain the image density and not diffuse to the region having a different characteristic.

The invention of claim 9 is a method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to spread the error data according to the distribution ratio,
An image processing method characterized by this.

  In this way, by diffusing error data in the region having the same characteristic, it is possible to control to retain the image density and not diffuse to the region having a different characteristic.

The invention of claim 10 is a method of diffusing error data in the error diffusion method,
In the process of adding error data,
According to the determination method,
Judgment is made during the process of adding error data,
If it is determined to add error data, the error data is added to the image data.
If it is determined that the error data is not added, the error data is not added to the image data.
An image processing method characterized by this.

  In this way, by diffusing error data in the region having the same characteristic, it is possible to control to retain the image density and not diffuse to the region having a different characteristic.

The invention of claim 11 is an image processing apparatus provided with an image processing method using an error diffusion method,
A judging means for judging the characteristics of the image data of the diffusion source and the diffusion destination;
Based on the result of the determination, image processing means for diffusing error data in the error diffusion method,
An image processing apparatus comprising:

  As described above, it is possible to provide an apparatus that can determine an error diffusion method by determining the characteristics of image data.

According to a twelfth aspect of the present invention, in the determination means,
Storage means for holding image data or attribute data or information for determining characteristics of image data for use in determination during image processing;
An image processing apparatus comprising:

  In this way, it is possible to provide an apparatus that can be referred to during image processing by maintaining the characteristics of image data.

According to a thirteenth aspect of the present invention, in the determination means,
Calculate the difference in image data between pixels,
A judging means for judging the difference,
An image processing apparatus comprising:

  As described above, it is possible to provide an apparatus that can determine a change in color tone of image data by calculating a difference between the image data.

According to a fourteenth aspect of the present invention, in the determination means,
Calculate the difference in image data between pixels,
A judging means for judging by comparing the difference with a certain threshold value;
An image processing apparatus comprising:

  In this way, by comparing the difference between the image data with a certain threshold value, it is possible to provide an apparatus that can detect a point where the change in color tone of the image data is large.

According to a fifteenth aspect of the present invention, in the determination means,
Determination means for determining from attribute data included in image data of pixels;
An image processing apparatus comprising:

  Thus, an apparatus that can determine the characteristics of an image can be provided by referring to attribute data.

The invention of claim 16 is the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to diffuse the error data,
An image processing apparatus characterized by this.

  As described above, it is possible to provide an apparatus capable of performing control so as not to diffuse into regions having different characteristics by processing so that error data is not diffused into regions having different characteristics of image data.

The invention of claim 17 is the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Diffusion data is diffused to the nearest area among the areas judged to diffuse error data.
An image processing apparatus characterized by this.

  As described above, it is possible to provide an apparatus capable of performing control so as not to diffuse into regions having different characteristics by processing so that error data is not diffused into regions having different characteristics of image data.

The invention of claim 18 is the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
The diffusion data is diffused to the unprocessed area among the areas determined to diffuse the error data.
An image processing apparatus characterized by this.

  As described above, it is possible to provide an apparatus capable of performing control so as not to diffuse into regions having different characteristics by processing so that error data is not diffused into regions having different characteristics of image data.

The invention of claim 19 is the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to spread the error data according to the distribution ratio,
An image processing apparatus characterized by this.

  As described above, it is possible to provide an apparatus capable of performing control so as not to diffuse into regions having different characteristics by processing so that error data is not diffused into regions having different characteristics of image data.

The invention of claim 20 is the image processing means,
In the process of adding error data,
By the determination means,
Judgment is made during the process of adding error data,
If it is determined to add error data, the error data is added to the image data.
If it is determined that the error data is not added, the error data is not added to the image data.
An image processing apparatus characterized by this.

  As described above, it is possible to provide an apparatus capable of performing control so as not to diffuse into regions having different characteristics by processing so that error data is not diffused into regions having different characteristics of image data.

  As described above, the image processing method according to the present invention has the following functions.

  In this image processing method, the image data of the pixel to be subjected to image processing is compared with the image data of surrounding pixels, and an error is not diffused to a region having a different characteristic, but another characteristic is diffused to a similar region.

  By performing this control, it is possible to prevent the occurrence of bleeding and isolated points in images such as character data and line drawings, which are likely to occur when using the error diffusion method, and to perform processing with density retention and edge enhancement. it can.

  Embodiments of the present invention will be described below.

(First embodiment)
FIG. 5 is a block diagram showing an image processing procedure according to the present invention. Each component will be described below.

  The color information input here represents the density of red (R), green (G), and blue (B), and is represented by a vector quantity (R, G, B).

The output color information represents the density of yellow (Y), magenta (M), and cyan (C), and is represented by a vector quantity (Y, M, C). These values are 8 bits for each color,
0 ≦ R ≦ FF
0 ≦ G ≦ FF
0 ≦ B ≦ FF
Also,
0 ≦ Y ≦ FF
0 ≦ M ≦ FF
0 ≦ C ≦ FF
And

  <Input γ Correction> γ correction is performed so that the input RGB signal matches the output characteristics of the device. The corrected signals are Rg, Gg, and Bg.

  <Color conversion processing> RGB data is converted into CMY data. This block has a color conversion table, and the output value is determined with reference to the time table for the input.

  <Output γ conversion> The density of the input and the amount of ink actually applied do not generally match, and are largely due to the characteristics of the ink. This is conversion for correcting the density and the amount of ink actually hit.

  <Binarization Processing> Inkjet printers hit ink dots on media. The density of the image is expressed by densely distributing the dots, thereby expressing the image. The process of converting this dot into information indicating whether or not to strike is a binarization process. In the binarization process, a process using an error diffusion method is performed. In the present invention, when the error diffusion method is performed, the following processing is performed.

  The control of the error diffusion method in the binarization process of the present invention will be described.

  FIG. 6 is a diagram showing the order of processing in the present invention. Reference numeral 601 denotes the entire area of the image to be processed. Reference numeral 602 denotes pixel data constituting an image.

  The process starts from the upper left pixel. Process from the position to the right. When processing is performed up to the rightmost pixel data, the processing of the lower row is similarly performed. This process is repeated, and finally the lower right pixel is processed and the process ends.

  FIG. 7 is a diagram showing error data source pixels to be added when processing a certain pixel a (i, j). In this embodiment, “Floyd & Steinberg type” error diffusion processing is assumed, but the present invention is not limited to this method.

  In FIG. 7, the pixel to which the diffusion data source is referred when processing a certain pixel a (i, j) is shown, but which pixel should be referred to when processing the pixel data at the end in the image. The point is not considered. FIG. 8 is a diagram showing a pixel to be referred to when this pixel is processed when there is pixel data at the end in the image. (A) When processing the pixel data 810 at the upper left corner in the image, since there is no pixel data to be referred to, an initially set process is performed. (B) When processing pixel data 820 at the upper end, reference is made to the pixel data 821 in the figure. (C) When processing the pixel data 830 at the upper right end, reference is made to the pixel data 831 in the figure. (D) When processing the pixel data 840 on the left end, reference is made to the pixel data 841 and 842 in the figure. (E) When processing pixel data 850 that is not in contact with the end, reference is made to the pixel data 851 to 854 in the figure. (F) When processing the pixel data 860 at the right end, the pixel data 861 to 863 in the figure are referred to. (G) When processing the pixel data 870 at the lower left corner, reference is made to the pixel data 871 and 872 in the figure. (H) When processing pixel data 880 at the lower end, reference is made to pixel data 881 to 884 in the figure. (I) When processing the pixel data 890 at the lower right corner, the pixel data 891 to 893 in the figure are referred to.

  In this embodiment, the range to be referred to when processing a certain pixel is shown in FIG. 7, but when the diffusion method is different from the method described in this embodiment (for example, when the processing order is the column direction or the row direction is Even when processing is performed over a plurality of rows), the pixel data is not limited to the range shown in FIG. Further, the reference method when the pixel to be processed is at the end is not limited to the method shown in FIG.

  In this embodiment, the unit to be processed is a pixel. However, when the present invention is carried out, an arbitrary region may be used instead of a pixel unit.

  FIG. 9 is a diagram showing the relationship between the error data diffusion source and the diffusion destination in the first embodiment. The coordinates of the image data of the diffusion source are (k, m) and the coordinates of the image data of the diffusion destination are (i, j). The upper figure shows the relationship with surrounding pixels when error data is diffused. In the figure, error data e (k, m) of the pixel 901 is multiplied by a predetermined coefficient α that differs depending on the diffusion destination and added to the pixel data of the diffusion destination. At this time, the addition in the (0, 1) direction is (1), the addition in the (1, -1) direction is (2), and the addition in the (1, 0) direction is (3), (1, 1 ) Addition in the direction is (4). The subsequent pixel processing sequence proceeds in the order of 902 → 903 → 904 → 905.

  On the other hand, the lower diagram shows the relationship with surrounding pixels when adding error data. In the figure, reference numeral 911 denotes pixel data to which error data is added. (1), (2), (3), and (4) in the two figures correspond to each other. The order of addition is (4), (3), (2), and (1).

  FIG. 10 is a diagram showing a diffusion method when there is a region that cannot be diffused in the present embodiment. In the present invention, it is determined whether or not the error data is to be diffused in the region where the error data is to be diffused. If it is determined that the error data is not to be diffused, diffusion is performed in other regions. The method will be described.

  Normally, error data is diffused as shown in (1), (2), (3), and (4) according to the diffusion coefficient as shown in (a).

  (B) When not diffusing to 1012, that is, when not diffusing (1), diffusing to adjacent 1015 is performed.

  (C) When not diffusing to 1023, that is, when not diffusing in (2), diffusion is performed on adjacent 1024.

  (D) When not diffusing to 1034, that is, when not diffusing (3), diffusing to adjacent 1035 is performed.

  (E) When not spreading to 1045, that is, when not spreading (4), since the other areas 1042 to 1044 have already been processed, there is no area for passing data, and the error data is lost. .

  FIG. 11 is a diagram showing definitions of symbols indicating image data in the present embodiment.

a (i, j) For image data of each color,
The image data of yellow (Y) is converted into Y (i, j)
The image data of magenta (M) is converted to M (i, j)
Cyan (C) image data is converted to C (i, j)
far. Further, threshold data for determining whether or not to add error data is set to yellow (Y) threshold data.
The threshold data of magenta (M) is Mm
The threshold value data of cyan (C) is Mc
far.

  (Expression 11-1) is an expression indicating that the elements of the image data a (i, j) are composed of yellow, magenta, and cyan image data.

  (Expression 11-2) is an expression indicating that the calculation of the image data a (i, j) calculates the image data amounts of yellow, magenta, and cyan which are the respective elements.

  (Expression 11-3) is an expression showing an example of the calculation of the image data a (i, j).

  (Expression 11-4) is an expression indicating the absolute value of the difference between the image data a (i, j).

  (Expression 11-5) is a diagram showing that the threshold value M is composed of yellow threshold value data (My), magenta threshold value data (Mm), and cyan threshold value data (Mc).

  FIG. 12 is a flowchart showing the processing order in the first embodiment. When the processing of the pixel of a (i, j) is started, the difference between a (i, j) and a (i-1, j-1) image data is calculated in S1201. If the difference is less than or equal to the threshold value M in each element (C, M, Y), the process proceeds to S1202. If the difference is greater than or equal to the threshold value M even for one color of each element, the process proceeds to S1203. In S1202, the error data of a (i-1, j-1) is added to the image data of a (i, j), and the process proceeds to S1203.

  In step S1203, the difference between a (i, j) and a (i-1, j) image data is calculated. If the difference is less than or equal to the threshold value M in each element, the process proceeds to S1204. In S1204, the error data of a (i-1, j) is added to the image data of a (i, j), and the process proceeds to S1205. In S1203, when the difference is equal to or greater than the threshold value M even in one color among the elements, the process proceeds to S1210. In S1210, the error data of a (i-1, j) is added to the error data to be added when the processing of a (i, j + 1) is performed.

  In step S1205, the difference between a (i, j) and a (i-1, j) image data is calculated. If the difference is less than or equal to the threshold value M in each element, the process proceeds to S1206. In S1206, the error data of a (i-1, j + 1) is added to the image data of a (i, j), and the process proceeds to S1207. If the difference is equal to or greater than the threshold value M even in one color among the elements in S1205, the process proceeds to S1211. In S1211, the error data of a (i-1, j + 1) is added to the error data to be added when the processing of a (i, j + 1) is performed.

  In step S1207, the difference between a (i, j) and a (i, j-1) image data is calculated. If the difference is less than or equal to the threshold value M in each element, the process proceeds to S1208. In S1208, the error data of a (i, j−1) is added to the image data of a (i, j), and the processing of a (i, j) is terminated. If the difference is equal to or greater than the threshold value M even in one color among the elements in S1207, the process proceeds to S1212. In step S1212, the error data a (i, j-1) is added to the error data to be added when the process a (i-1, j) is performed. And the process of a (i, j) is complete | finished.

(Second embodiment)
In the first embodiment, as shown in FIG. 9, the original image data which is the reference data is held, the difference between the image data is taken, and the control is performed so that the error data is not diffused in the regions having different color tones. However, this processing needs to hold data that understands the characteristics of the image, and if the same control is performed with `` Jarvis, Judice & Ninke type '' that expands the error diffusion area, the memory area will be occupied and executed It causes a decrease in speed.

  For this reason, in this embodiment, processing is performed by adding attribute data to the image data. By this control, more accurate processing can be performed without retaining the original image data for reference.

  FIG. 13 is a diagram showing definitions of symbols indicating image data in the present embodiment.

a (i, j) For image data of each color,
The image data of yellow (Y) is converted into Y (i, j)
The image data of magenta (M) is converted to M (i, j)
Cyan (C) image data is converted to C (i, j)
Attribute data is Z (i, j)
far.

  (Expression 13-1) is an expression indicating that the elements of the image data a (i, j) include yellow, magenta, and cyan image data, and attribute data indicating image characteristics.

  FIG. 14 is a flowchart showing the processing order in the second embodiment. When processing of the pixel a (k, m) which is the diffusion source pixel is started, the attribute data of a (k, m) and a (k, m + 1) are compared in S1401. If the attribute data matches, the process proceeds to S1402. In S1402, the error data e (k, m) × α (0, 1) of a (k, m) is added to a (k, m + 1), and the process proceeds to S1403. If the attribute data is different in S1401, the process proceeds to S1411. In S1411, the error data is added to the error data diffused to a (k + 1, m + 1), and the process proceeds to S1403.

  In S1403, the attribute data of a (k, m) and a (k + 1, m−1) are compared. If the attribute data matches, the process proceeds to S1404. In S1404, the error data e (k, m) × α (1, −1) of a (k, m) is added to a (k + 1, m−1), and the process proceeds to S1405. If the attribute data is different in S1403, the process proceeds to S1412. In S1412, the error data is added to the error data diffused to a (k + 1, m), and the process proceeds to S1405.

  In S1405, the attribute data of a (k, m) and a (k + 1, m) are compared. If the attribute data matches, the process proceeds to S1406. In S1406, the error data e (k, m) × α (1, 0) of a (k, m) is added to a (k + 1, m), and the process proceeds to S1407. If the attribute data is different in S1405, the process proceeds to S1413. In S1413, the error data is added to the error data diffused to a (k + 1, m + 1), and the process proceeds to S1407.

  In S1407, the attribute data of a (k, m) and a (k + 1, m + 1) are compared. If the attribute data matches, the process proceeds to S1408. In S1408, the error data e (k, m) × α (1, 1) of a (k, m) is added to a (k + 1, m + 1), and the process is completed. If the attribute data is different in S1407, the process is completed as it is.

(Third embodiment)
In the second embodiment, as shown in FIG. 14, control is performed so that error data is not diffused in regions having different characteristics using attribute data. However, in this process, when the attribute data of a (k, m) and a (k + 1, m + 1) are different, the error data to be diffused to a (k + 1, m + 1) is lost without being added anywhere and the image is lost. It is not possible to preserve the concentration. In this embodiment, processing is performed so that the concentration is preserved.

  FIG. 15 is a flowchart showing the processing order in the third embodiment.

  Here, Sum is a variable for taking and summing the diffusion coefficients and normalizing them.

  When processing of the pixel a (k, m), which is the diffusion source pixel, is started, the attribute data of a (k, m) and a (k, m + 1) are compared in S1501. If the attribute data match as a result of the comparison, the process advances to step S1502. If they do not match, the process proceeds to S1503 without executing S1502. In S1502, α (0, 1), which is a coefficient that diffuses to a (k, m + 1), is assigned to Sum.

  In S1503, the attribute data of a (k, m) and a (k + 1, m−1) are compared. If the attribute data match as a result of the comparison, the process advances to step S1504. If they do not match, the process proceeds to S1505 without executing S1504. In S1504, α (1, −1), which is a coefficient that diffuses to a (k + 1, m−1), is added to Sum.

  In S1505, the attribute data of a (k, m) and a (k + 1, m) are compared. If the attribute data match as a result of the comparison, the process advances to step S1506. If they do not match, the process proceeds to S1507 without executing S1506. In S1506, α (1,0), which is a coefficient that diffuses to a (k + 1, m), is added to Sum.

  In S1507, the attribute data of a (k, m) and a (k + 1, m + 1) are compared. If the attribute data match as a result of the comparison, the process advances to step S1508. If they do not match, the process proceeds to S1509 without executing S1508. In S1508, α (1, 1), which is a coefficient that diffuses to a (k + 1, m + 1), is added to Sum.

  In S1509, the value of Sum is confirmed. If Sum = 0, it means that there is no region to be diffused, and the process of a (k, m) is terminated. If Sum ≠ 0, there is a region to be diffused, so the process proceeds to S1511 and error diffusion processing is performed.

  In S1511, the attribute data of a (k, m) and a (k, m + 1) are compared. If the attribute data match as a result of the comparison, the process advances to step S1512. If they do not match, the process proceeds to S1513 without executing S1512. In S1512, the error obtained by normalizing the diffusion coefficient by Sum is added to a (k, m + 1), and the process proceeds to S1513.

  In S1513, the attribute data of a (k, m) and a (k + 1, m−1) are compared. If the attribute data match as a result of the comparison, the process advances to step S1514. If they do not match, the process proceeds to S1515 without executing S1514. In S1514, an error obtained by normalizing the diffusion coefficient by Sum is added to a (k + 1, m−1), and the process proceeds to S1515.

  In S1515, the attribute data of a (k, m) and a (k + 1, m) are compared. If the attribute data match as a result of the comparison, the process proceeds to S1516. If they do not match, the process proceeds to S1517 without executing S1516. In S1516, the error obtained by normalizing the diffusion coefficient by Sum is added to a (k + 1, m), and the process proceeds to S1517.

  In S1517, the attribute data of a (k, m) and a (k + 1, m + 1) are compared. If the attribute data match as a result of the comparison, the process advances to step S1518. If they do not match, the process proceeds to S1519 without executing S1518. In S1518, the error obtained by normalizing the diffusion coefficient by Sum is added to a (k + 1, m + 1), and the process proceeds to S1519.

  In S1519, Sum = 0 is substituted to prepare for the next process.

  FIG. 16 is a generalized expression of the processing in the third embodiment. δ is a unit impulse function.

  FIG. 17 is a diagram showing how the first to third embodiments are actually processed at the pixel level.

  1701 shows a state where normal error diffusion is performed.

  1702 shows that the error cannot be diffused to the right and lower right pixels but diffused to the lower and lower left pixels.

  Reference numeral 1703 shows that the error cannot be diffused to the right pixel, but diffused to the lower right, lower and lower left pixels.

  Reference numeral 1704 indicates that the error cannot be diffused to the lower left pixel but diffused to the right, lower right, and lower pixels.

  Reference numeral 1705 indicates that the error cannot be diffused to the right, lower right, and lower pixels but diffused to the lower left pixels.

  In the first to third embodiments, 1703 pixels are shown as an example of how they are diffused.

  In the first and second embodiments, since diffusion to the right pixel is not possible, diffusion is performed to the lower right pixel.

  In the third embodiment, diffusion is performed in proportion to the diffusion coefficient.

  FIG. 18 is a diagram showing how the flow of the first to third embodiments functions when processing a specific image.

  This image is an image in which characters are drawn in an image having a density gradient. Processing is performed in the order indicated by the arrows in the figure. Since the process starts with a smooth density gradient, a normal error diffusion process is performed. When entering the character area at the position (1), a difference in image data occurs. By the control of the present invention, the error data is not diffused in the areas having greatly different color tones. Similarly, in the first embodiment, error data is not diffused at the positions (2) and (3). In (4), since the difference is equal to or smaller than the threshold value, the error data is diffused. According to this control, in (1), (2), and (3), the error data of the image data does not enter the character data, or the error data of the character data does not enter the image data. In this case, it is possible to prevent bleeding and isolated points from occurring. Further, by implementing the second and third embodiments, it is possible to control so that error data is not diffused even when the color tone is similar but the attribute data is different as in (4).

It is a figure which shows the diffusion method of the error data of an error diffusion method. It is a figure explaining an error diffusion method. It is a figure which shows the diffusion area | region and weighting coefficient of "Floyd & Steinberg type" in an error diffusion method. It is a figure which shows the diffusion area and weighting coefficient of "Jarvis, Judice & Ninke type" in an error diffusion method. FIG. 5 is a diagram illustrating an image processing procedure in the first embodiment of the present invention. FIG. 6 is a diagram illustrating a pixel processing order in the first embodiment. In the first embodiment, it is a diagram showing a diffusion source of error data at the time of processing a certain pixel. In the first embodiment, it is a diagram showing a diffusion source of error data when a certain pixel is at the end. In a 1st Example, it is a figure which shows the relationship between error diffusion and a surrounding pixel. In a 1st Example, it is a figure which shows the spreading | diffusion method of the error in each case. In the first embodiment, this is a definition formula for explaining the processing. 5 is a flowchart illustrating a processing procedure in the first embodiment. In a 2nd Example, it is a definition formula for explaining processing. In the 2nd example, it is a flow chart which shows a processing procedure. 10 is a flowchart illustrating a processing procedure in the third embodiment. In the third embodiment, the processing method is generalized. It is the figure which showed the mode of the process in the pixel level in the 1st-3rd Example. It is the figure which showed the mode of the process in the image level in the 1st-3rd Example.

Explanation of symbols

101 Diffusion source pixels 102 to 105 Destination pixels 501 Input γ conversion unit 502 Color conversion 503 Output γ conversion unit 504 Binarization 601 Pixel 602 Image data whole area 701 Processing target pixel 702 to 705 Error data diffusion source pixel 810 Processing target Pixel 820 Processing target pixel 821 Reference pixel 830 Processing target pixel 831 Reference pixel 840 Processing target pixel 841, 842 Reference pixel 850 Processing target pixel 851-854 Reference pixel 860 Processing target pixel 861-863 Reference pixel 870 Processing target pixel 871, 872 Reference Pixel 880 Processing target pixel 881 to 884 Reference pixel 890 Processing target pixel 891 to 893 Reference pixel 901 Diffusion source pixel 902 to 905 Destination pixel 911 Processing target pixel 912 to 915 Error data diffusion source pixel 1001 Diffusion source pixel 10 2 to 1005 Destination target pixel 1011 Diffusion source pixel 1012 to 1015 Destination target pixel 1021 Diffusion source pixel 1022 to 1025 Destination target pixel 1031 Diffusion source pixel 1032 to 1035 Destination target pixel 1041 Diffusion source pixel 1042 to 1045 Destination target Pixel 1701 Normal error diffusion pixel 1702 Error cannot be diffused to the right and lower right pixels, and error diffused to the lower and lower left pixels 1703 Error cannot be diffused to the right pixel, right Pixels 1diffused to the lower, lower and lower left pixels 1704 Errors cannot be diffused to the lower left pixels, but pixels diffused to the right, lower right and lower pixels 1705 Right, lower right and lower pixels Pixels that cannot diffuse the error and diffuse to the lower left pixel

Claims (20)

An image processing method using an error diffusion method,
Determine the characteristics of the source and destination image data,
Based on the result of the determination, the error data in the error diffusion method is diffused.
An image processing method. In the determination method,
Holds information for determining the characteristics of image data or attribute data or image data for use in determination during image processing.
The image processing method according to claim 1. In the determination method,
Calculate the difference in image data between pixels,
Judge based on the difference value,
The image processing method according to claim 1. In the determination method,
Calculate the difference in image data between pixels,
Judging based on the result of comparing the difference with a certain threshold,
The image processing method according to claim 1. In the determination method,
Judging from the attribute data included in the pixel image data,
The image processing method according to claim 1. A method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to diffuse the error data,
The image processing method according to claim 1. A method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Diffusion data is diffused to the nearest area among the areas judged to diffuse error data.
The image processing method according to claim 1. A method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
The diffusion data is diffused to the unprocessed area among the areas determined to diffuse the error data.
The image processing method according to claim 1. A method of diffusing error data in the error diffusion method,
In the process of diffusing error data,
According to the determination method,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to spread the error data according to the distribution ratio,
The image processing method according to claim 1. A method of diffusing error data in the error diffusion method,
In the process of adding error data,
According to the determination method,
Judgment is made during the process of adding error data,
If it is determined to add error data, the error data is added to the image data.
If it is determined that the error data is not added, the error data is not added to the image data.
The image processing method according to claim 1. An image processing apparatus having an image processing method using an error diffusion method,
A judging means for judging the characteristics of the image data of the diffusion source and the diffusion destination;
Based on the result of the determination, image processing means for diffusing error data in the error diffusion method,
An image processing apparatus comprising: In the determination means,
Storage means for holding image data or attribute data or information for determining characteristics of image data for use in determination during image processing;
The image processing apparatus according to claim 11, further comprising: In the determination means,
Calculate the difference in image data between pixels,
A judging means for judging the difference,
The image processing apparatus according to claim 11, further comprising: In the determination means,
Calculate the difference in image data between pixels,
A judging means for judging by comparing the difference with a certain threshold value;
The image processing apparatus according to claim 11, further comprising: In the determination means,
Determination means for determining from attribute data included in image data of pixels;
The image processing apparatus according to claim 11, further comprising: In the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to diffuse the error data,
The image processing apparatus according to claim 11. In the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Diffusion data is diffused to the nearest area among the areas judged to diffuse error data.
The image processing apparatus according to claim 11. In the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
The diffusion data is diffused to the unprocessed area among the areas determined to diffuse the error data.
The image processing apparatus according to claim 11. In the image processing means,
In the process of diffusing error data,
By the determination means,
Judgment is made during the process of diffusing error data,
If there is an area that is determined to diffuse error data and an area that is determined not to diffuse,
Diffusion data determined not to diffuse error data,
Spread the diffusion data to the area that is determined to spread the error data according to the distribution ratio,
The image processing apparatus according to claim 11. In the image processing means,
In the process of adding error data,
By the determination means,
Judgment is made during the process of adding error data,
If it is determined to add error data, the error data is added to the image data.
If it is determined that the error data is not added, the error data is not added to the image data.
The image processing apparatus according to claim 11.

Images