JP4850475B2 - Method for filtering pixels in an image - Google Patents

Description

  The present invention relates generally to digital signal processing, and more particularly to classifying pixels in an image and filtering the pixels according to the classification.

  Compression is used in many imaging applications, including digital cameras, broadcast TVs and DVDs, to increase the number of images that can be stored in memory or to reduce transmission bandwidth. When the compression ratio is high, visible artifacts may occur in the reconstructed image due to the side effects of quantization and coefficient truncation. A realistic solution is to filter the reconstructed image to suppress visible artifacts and to guarantee the subjective quality of the reconstructed image.

  ITU-TH. Most video coding standards such as 26x and MPEG-1 / 2/4 use block-based processes. At high compression ratios, some artifacts are visible due to the processing based on the underlying blocks. The most common artifacts are blocking and ringing.

  Blocking artifacts appear as lattice noise along the block boundaries of the monotone region of the restored image. Blocking artifacts occur because adjacent blocks are processed separately and the pixel intensities at the block boundaries are not perfectly aligned after restoration. Ringing artifacts are more prominent along the edges of the restored image. This effect is known as the Gibbs phenomenon and is caused by truncation of the high frequency coefficient by the quantized AC coefficient.

  Many post-processing techniques are known to remove these coding artifacts.

  The spatial domain method is US Pat. No. 6,539,060 “Image data post-processing method for reducing quantization effect, apparatus therefor” issued to Lee et al. On March 25, 2003, 2002 to Osa. US Pat. No. 6,496,605 issued December 17, 2000, “Block deformation removing filter, image processing apparatus using the same, method of filtering image signal, and storage medium for storing software therefor”, Konstantinides US Pat. No. 6,320,905 issued on November 20, 2001, “Postprocessing system for removing blocking artifacts in block-based codecs”, US patent issued on January 23, 2001 to Cheung et al. No. 6,178,205 “Video postfiltering with motion-compensated temporal filtering and / or spatial-adaptive filtering”, December 26, 2000 to Sugahara et al. Issued on July 6, 1999 to Gupta et al., US Pat. No. 6,167,157, “Method of reducing quantization noise generated during a decoding process of image data and device for decoding image data” U.S. Pat. No. 5,920,356, “Coding parameter adaptive transform artifact reduction process”.

  The Discrete Cosine Transform (DCT) domain method is known as “Blocking artifact detection and reduction in compressed data” (IEEE Transactions on Circuits and Systems for Video Technology, Vol. 12, October 2002) by Triantafyllidis et al., “Adaptive post- filtering of transform coefficients for the reduction of blocking artifacts "(IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, May 2001).

  Wavelet-based filtering methods are described in Xiong et al., “A deblocking algorithm for JPEG compressed images using overcomplete wavelet representations” (IEEE Transactions on Circuits and Systems for Video Technology, Vol. 7, No. 2, August 1997), and Lang et al. "Noise reduction using an undecimated discrete wavelet transform" (Signal Processing Newsletters, Vol. 13, January 1996).

  The iterative method is Paek et al., "A DCT-based spatially adaptive post-processing technique to reduce the blocking artifacts in transform coded images" (IEEE Transactions on Circuits and Systems for Video Technology, Vol. 10, February 2000), Paek et al. "On the POCS-based post-processing technique to reduce the blocking artifacts in transform coded images" (IEEE Transactions on Circuits and Systems for Video Technology, Vol. 8, June 1998).

  A fuzzy rule-based filtering method is proposed by Arakawa for “Fuzzy rule-based signal processing and its application to image restoration” (IEEE Journal on selected areas in communications, Vol. 12, No. 9, December 1994), and Giura et al. US Pat. No. 6,332,136 issued on Dec. 18, 2001, “Fuzzy filtering method and associated fuzzy filter”.

  Most prior art methods only deal with blocking noise. Such a method has no effect on ringing noise. Some methods, such as wavelet-based methods, can reduce ringing but blur the entire restored image. Prior art fuzzy rule based filtering methods only deal with white Gaussian noise.

  The prior art methods described above operate on the pixels separately and apply the same filter to each pixel. Such methods generally do not consider the underlying content of the image. Therefore, these filters either over-smooth and blur the image to remove artifacts, or, if minimal smoothing is applied, they cannot reduce the artifacts sufficiently.

  Another problem with such a method is computational complexity. For example, wavelet based methods require low and high pass filtering operations based on 8 convolutions to obtain wavelet images. Next, a deblocking operation is performed on these wavelet images to remove blocking artifacts. In order to reconstruct a deblocked image, low-pass and high-pass filtering operations based on 12 convolutions are required. Therefore, the method requires a filtering operation based on a total of 20 convolutions. This computational cost cannot meet the requirements of real-time processing. Similar to this wavelet-based method, the DCT domain method also has a high computational complexity. In the case of low-pass filtering using a 5 × 5 window, 25 DCT operations are required to process a single 8 × 8 block. Such high complexity is also impractical for real-time processing. The computational cost of the iterative method is even higher than the above two methods. For fuzzy rule based filtering methods, the iterative method requires a large number of filter parameters and additional training data.

  In view of the problems of the prior art methods, it is desirable to provide a new filtering structure that achieves higher image / video image quality with low computational complexity.

  The method first filters the pixels in the image by dividing the image into blocks. Identify edge blocks. The variance of the intensity of each pixel in each edge block is obtained. Next, each pixel in each edge block is filtered by a filter that depends on the variance of the pixels.

  FIG. 1 illustrates a system and method 100 according to the present invention. The system is independent of any image or video decoder. The system does not rely on any coding parameters embedded in the compressed image or video. The present invention focuses on local features in the image. The method according to the invention extracts local features and classifies them. Next, if the image is a restored image, the pixels can be selectively and adaptively filtered using the classified features.

  The input is an image 201. This method is effective for any image format (eg, YUV or RGB). It should be understood that the system can process image sequences such as in video. For example, the image 201 may be part of progressive video or interlaced video. It should also be noted that the input image may be an original image that has never been compressed or a restored image.

  However, if the input image is a decompressed image obtained from a compressed image, and the compressed image is derived from an original image that was compressed using a block-based compression process, the previous compression will restore Image 201 has blocking artifacts caused by separate quantization of the DCT coefficient block of the compressed image. Therefore, the restored image 201 has a block break in the space value between adjacent blocks. Ringing artifacts can also occur along the edges in the restored image.

  In order to reduce these artifacts while retaining the original original information, the filtering according to the present invention is based on the local feature classification in the input image 201. Furthermore, filtering is adaptive to this classification.

Dispersed image From a statistical point of view, the distribution of intensity values of pixels indicates the features of the restored image. The average intensity value m of the image represents the DC component of the image. The average intensity value can be measured by the following formula.

Here, M and N are the width and height converted into the number of pixels of the restored image, and p _{xi, j} is the probability of the pixel occurring at the position of i, j.

  The average power of the restored image is an average square value expressed by the following equation.

  The average variation is the variance expressed by the following equation.

  The mean square represents the average power of the DC component in the image, and the variance represents the average power of the AC frequency component in the compressed image 201. Thus, the variance of intensity values is used as a measure of AC power variation representing energy in the image.

  If the variance of a pixel is high, the pixel is likely related to an edge. If the variance is low, the pixel is part of a homogeneous area of the image, for example a smooth background. Therefore, the variance indicates the characteristics of local features in the image.

  Both blocking artifacts and ringing artifacts are due to local feature characteristics, i.e., artifacts appear near block boundaries or edges, so local features are sufficient to show these artifacts. is there. Accordingly, classification and filtering according to the present invention is based on an energy distribution measured by local dispersion of pixel intensity values as described in equation (3) above. The characteristic of the feature is obtained by extracting (210) the intensity value 211 as follows.

  As shown in FIG. 3, each pixel 302 in the restored image 201 is scanned with a smooth 3 × 3 filter 301. Scanning can be performed in raster scan order. For each central pixel 301 of the filter, the average and variance of the intensity values 211 are determined according to equations (1) to (3) (220). The variance value forms a variance image 401. From a geometric point of view, the local variance reflects the gradient of the restored image at each pixel location. This is because the gradient at the edge in the image is very high.

  As shown in FIG. 4, the extraction and scanning of the feature part converts the restored image 201 in the spatial region in which the pixel has the intensity value 211 into the dispersed image 401 in the energy region in which the pixel has the variance 411.

Pixel Classification As shown in FIG. 5, the pixel 211 having a variance less than the first threshold value_1 is classified as class_0 501 (230). These pixels correspond to homogeneous or “smooth” areas in the image. Pixels having a variance greater than the second threshold_2 are classified as class_1 502. These pixels are most likely to correspond to edges. Pixels having a variance between these two thresholds are classified as class_2 503. These pixels can be considered as ringing noise or texture depending on the characteristics of neighboring pixels. The adaptive filtering according to the present invention is performed according to the above classification.

Classification of Blocks The pixel blocks are also classified into a “smooth” block 241, a “texture” block 242 and an “edge” block 243 according to the variance value in the variance image 401 (240). Block classification 240 can be performed based on the total variance within each block or by counting the number of pixels of each class within the block. For example, when all the pixels in the block are class_0, the block is classified as a smooth block. When at least one pixel in the block is class_1, the block is classified as an edge block. If the block has both class_0 and class_2 pixels, the block is classified as a texture block.

Blocking Artifact Detection The best known image and video compression standards are based on DCT coding of pixel blocks. Block-based encoding divides an image completely into pixel blocks, usually one block of 8x8 pixels. The pixels of each block are converted to DCT coefficients separately. Next, the DCT coefficients are quantized according to a predetermined quantization matrix. Due to the separate encoding, blocking artifacts are visible at the block boundaries.

  FIG. 6 shows a method for detecting (250) blocking artifacts on an 8 × 8 block 600. Outer pixels are indicated by stars 601 and “inner” pixels are indicated by black circles 602. The inner pixel is located adjacent to and parallel to the top row and left column in the block. Detection 250 is performed from left to right and from top to bottom for each block.

  In the presence of blocking artifacts, the slope of the variance of the outer pixel 601 is approximately the same as the inner pixel 602. The criteria for determining the presence of blocking artifacts are as follows.

  The sign (sign) is either +1 or -1. The above test distinguishes between blocking artifacts and edges at block boundaries.

Deblocking Filter As shown in FIG. 7, blocking artifacts are removed by filtering the detected block boundaries in the reconstructed image (260). When a blocking artifact is detected, a one-dimensional low-pass (smoothing) filter is adaptively applied to the pixel along the block boundary 601. The size of the filters 702, 704, 706 (eg, 2, 4, 6 or more pixels) corresponds to the gradient at the block boundary. Pixels with large gradient values (ie, edge pixels) are excluded from the filtering operation to avoid blurring edges and textures.

By applying the fuzzy filter 271, the deringing 270 acts only on the edge block 243. The fuzzy filter according to the present invention is based on fuzzy transformation theory. See Nie et al., “Fuzzy transformation and its applications” (IEEE International Conference on Image Processing, Barcelona, Spain, September, 2003).

In fuzzy transformation, the relationship between spatial samples x _i (eg pixel intensity values) and order statistics x _j is established by a real-valued Gaussian function μ _F (a, b), where i is the spatial index i = 1, 2,. . . , N and j = 1, 2,. . . , N are order statistics, and x ₍₁₎ ≤ x ₍₂₎ ≤. . . ≦ x _(N) and the size of the observation or filtering window is N.

  The membership function has the following constraints.

  As a result, an N × N fuzzy space rank (SR) matrix defined by the following equation is obtained.

  Since the elements of the fuzzy SR matrix R (tilde) depend on the difference in values between each pair of pixels, the fuzzy SR matrix includes spread information embedded in the observed pixels.

  The original or “clear” spatial pixel can be “transformed” into fuzzy spatial pixels by multiplying the explicit order statistic vector by the row normalized fuzzy SR matrix. The resulting fuzzy space pixel also reflects spread information. The output 272 of the fuzzy filter 271 according to the present invention corresponds to a fuzzy central pixel of the observation or filtering window.

  The output of the filter can be obtained using the following simplified formula:

Here, x _c and x _c (tilde) are a clear central pixel and a blurred central pixel, respectively.

As suggested by the last expression in the output of the filter, no ordering operation is necessary. Therefore, the computational complexity of the fuzzy filter 271 is only slightly higher than that of the linear filter. The only additional calculation is to evaluate the value of the function between N-1 pairs of pixels. Note that μ _F (x _c , x _c ) = 1 for all pixels, so there is no need to determine.

In one embodiment of the invention, the real valued function μ _g (a, b) is defined by a Gaussian function:

  Here, the spread parameter ξ is 20.

FIG. 2 shows examples of several values of the Gaussian function representing the similarity between the values of the sample x _i (i = 1, 2,..., N) and the window center sample x _c . In this given example, x _c = 128, x ₁ = 180, x ₂ = 20, x ₃ = 100.

  From the above equation, it can be seen that the output of the fuzzy filter is a weighted average of the samples in the filtering window. The value of the Gaussian function, i.e. the measure of similarity between each sample including the center sample itself and the center sample, is used as the weight of the corresponding sample.

  Thus, the closer the sample value is to the center sample, the more weight is assigned to that sample. This has the effect that samples with similar values are further clustered around their local average, while samples with different values remain nearly intact. This is known as a clustering characteristic of fuzzy transformation.

  As a result, the fuzzy filter 271 according to the present invention has a smoothing function that is adaptive to the data, so that strong edges can be completely preserved while weak edges associated with annoying ringing artifacts can be removed. .

  FIG. 8 shows this function. In this example, the “step” signal input sample 801 is corrupted by uniformly distributed noise. Thus, the corrupted signal consists of two groups of distinct samples, one group localized around 0 and the other group localized around 1.

  After filtering by the fuzzy filter 271, each group containing samples of similar values is clustered more closely around the local average of that group, resulting in a filtered step signal 802. In this way, undesired perturbations in uniform regions are smoothed while the step edge is restored. Note that this example accurately simulates ringing artifacts around strong edges. Thus, this example shows how a fuzzy filter removes these artifacts and preserves edges.

Adaptive Filtering From equation (6) above, it can be seen that the blurred center pixel in the window is a weighted average. Each weight is given in particular by a Gaussian function as defined by the spread parameter ξ. The spread parameter controls the shape of the Gaussian function, ie the range of filtering. When the spread parameter ξ is large, the Gaussian function is relatively wide. This corresponds to a smoother filter. When the spread parameter ξ is small, the Gaussian function is narrow and the smoothness of filtering is lowered. The window size N has similar characteristics. Large windows have a stronger smoothing effect than small windows.

  Accordingly, the present invention provides an adaptive fuzzy filtering method. The window size N and the spread parameter ξ are adaptively determined according to the values in the dispersed image 401.

There are the following four possible combinations of the window size N and the spread parameter ξ.
Small window N and small ξ
Small window N and large ξ
Large window N and small ξ
Large window N and large ξ

  As a basic principle of the present invention, a small variance corresponds to a small window and a small spread parameter ξ, and a large variance corresponds to a large window and a large spread parameter ξ.

  FIG. 9 shows the steps of the adaptation method according to the invention. At start 910, the input to the method is the next classification block 901 (until end 990) and the corresponding restored image block 902.

  In step 920, it is determined whether or not the variance value in the variance image 401 is greater than 22. If true 921, the corresponding pixel is most likely on an edge. Therefore, in order to preserve the sharpness of the edge, this pixel is all-pass filtered. In basic fuzzy filtering, as described above, all edge pixels are filtered by the same filter having the same spread parameter.

On the contrary, if it is false, it is determined in step 930 whether the variance value is 22 or less and 10 or more. If true 931, the pixel is probably destroyed by ringing noise, so select a large spread parameter 940 (ξ is 30) and a large window size 941 (N is 5 × 5) . That is, a strong smooth fuzzy filter is used for filtering 970 to reduce ringing artifacts to the maximum.

On the other hand, if false, step 950 determines whether the variance is less than 10 and greater than or equal to 4. If true 951, the corresponding pixel may be in a “weak” edge region or it may be slightly destroyed by ringing noise. In this case, a small spread parameter 960 (ξ is 15) and a small window 961 (N is 3 × 3), ie a weak smooth fuzzy filter, is used for filtering 970. On the other hand, if the variance is less than 4, the pixel is in the smooth region, so all-pass filtering is applied to process the next pixel. After processing all the pixels, the filtered block is output (980), and the next block 901 is processed until the end 990.

  Various limits of variance, window, and spread parameters, such as (22,10,2), (5,3), and (30,15) can be varied for different applications, but still remain within the spirit and scope of the present invention. It should be noted that

Improved deblocking In conventional interlaced video, a single frame can be encoded using both a frame-based coding scheme and a field-based coding scheme. This increases the complexity of the artifact. In order to achieve better artifact removal and detail preservation, the present invention processes the two fields of each video frame separately. De-blocking in the vertical and horizontal directions using different schemes. Apply 1D adaptive fuzzy filters with different window sizes to remove horizontal and vertical blocking artifacts, respectively.

Detection of Vertical Blocking Artifacts As shown in FIG. 10, vertical blocking artifact detection is performed along each vertical block boundary 1010 that intersects a row 1011 of 8 × 8 pixels. The pixel intensity difference G0 is obtained by G0 = | x0−y7 |. Next, the difference in intensity between each pair of adjacent pixels on the left and right sides of the block boundary indicated by L1, L2, L3, L4 and R1, R2, R3, R4, respectively, is also obtained.
max (L1, L2, L3, L4) <G0 or max (R1, R2, R3, R4) <G0
If so, the line is marked and a boundary gap is detected along the line.

  After examining all eight boundary pixel pairs in row 1011 along vertical block boundary 1010, the number of marked rows (ie, boundary gaps) is greater than a predetermined threshold TH1 (eg, TH1 = 0). If so, block artifacts are detected at the current vertical block boundary, and 1D adaptive fuzzy filtering is performed across the boundary to eliminate vertical blocking artifacts, or filtering is omitted.

Vertical Blocking Artifact Removal As shown in FIG. 11, 1D filtering is performed only on marked rows that straddle block boundaries.
MAX (L1, L2, L3, L4) <G0
If this is the case (meaning that the boundary gap is obvious compared to the difference between adjacent pixels on the left), filter pixel 1111, ie y6, y7 and x0. Similarly,
MAX (R1, R2, R3, R4) <G0
If so, filter pixel 1112, ie, y7, x0 and x1.

  If the intensity difference between adjacent pixel pairs on one side of the boundary is greater than the boundary gap (which is likely to be caused by an edge in the input image), the boundary gap probably does not exist, so row filtering on this side is Note that it is not necessary. This also helps to preserve blocking artifacts that look like edges while preserving edges in the image.

1D Fuzzy Filter A filter used for smoothing vertical blocking artifacts is a 5-tap fuzzy filter 1113 centered on each pixel to be filtered (for example, y6). This fuzzy filter preserves strong edges located along block boundaries.

  The output of the fuzzy filter is expressed as follows.

Here, x _c is the center pixel of the filter window, and μ _L (·) is a piecewise linear function defined as follows.

Detection of Lateral Blocking Artifacts FIG. 12 illustrates a method of detecting lateral blocking artifacts in a pixel column that intersects the lateral block boundary 1201. G0 is the difference in image intensity of the boundary pixel pair (ie G0 = | x0−y7 |), U1, U2, U3, U4 and B1, B2, B3, B4 are above and below the horizontal boundary. This is the intensity difference between each adjacent pixel pair on the side. G _UL , G _UR , G _BL, and G _BR are the numbers of vertical boundary gaps at adjacent upper left, upper right, lower left, and lower right vertical boundaries, respectively.

  Detection of lateral blocking artifacts is performed along each lateral boundary of the 8 × 8 block. The method is similar to that used to detect longitudinal blocking artifacts, but additional conditions must be met before identifying the lateral blocking artifacts. The reason for adding these conditions is to more accurately detect blocking artifacts and to avoid filtering across lateral edges in the image that may exist along the block boundary. Filtering these real edges will result in annoying artifacts.

  First, the horizontal boundary gap is detected and counted using the same method as the detection and counting of the vertical boundary gap. However, this time, all operations are performed on each row spanning the current horizontal boundary. If the number of lateral boundary gaps detected along the current lateral boundary is less than a predetermined threshold TH2 (eg, TH2 = 5), filtering across this boundary is not performed. Otherwise, examine the four vertical block boundaries adjacent to the current horizontal boundary, i.e., the upper left, upper right, lower left and lower right vertical block boundaries to determine the current horizontal boundary Check for large vertical blocking artifacts around.

G _UL , G _UR , G _BL , and G _BR indicate the number of boundary gaps detected at the upper left, upper right, lower left, and lower right vertical boundaries, respectively. If at least one of the following conditions is met, it is assumed that large vertical blocking artifacts have also been detected, so that horizontal blocking artifacts occur at the current horizontal boundary.

Conditions (G _UL > TH2) and (G _UR > TH2)
(G _BL > TH2) and (G _BR > TH2)
(G _UL > 7) or (G _UR > 7) or (G _BL > 7) or (G _BR > 7)

  Since the vertical boundaries are processed before the horizontal boundaries, the number of vertical boundary gaps at each vertical boundary is known.

Horizontal Blocking Artifact Removal As shown in FIG. 13, 1D filtering is performed only on marked columns that span horizontal block boundaries. If MAX (U1, U2, U3, U4) <G0 (meaning that the boundary gap is clear compared to the difference between adjacent pixels above), filter pixel 1301, ie y6, y7. To do.

  Similarly, when MAX (B1, B2, B3, B4) <G0, the pixel 1302, that is, x0 and x1 is filtered. Note that, unlike filtering across vertical boundaries that filter both x0 and y7 on either side, only one boundary pixel (x0 or y7) is filtered on either side. This reduces filtering across the lateral edges. The filter used for smoothing the horizontal blocking artifact is a 3-tap fuzzy filter 1303 centered on each pixel to be filtered. The filter weight is determined using equation (7).

Block Classification As described above, block classification can significantly reduce local artifacts without reducing overall image quality. However, there are many other imaging applications where block classification is equally useful. For example, if the blocks are properly classified, overall higher compression ratios can be achieved by applying different compression ratios and compression techniques to different blocks. For example, higher compression ratios and simpler compression techniques can be applied to smooth blocks so that additional bandwidth and higher compression can be applied to blocks with more complex textures. Similarly, the efficiency of pattern recognition and object tracking can be increased by first discarding “low interest” blocks. Furthermore, the image search system can selectively use the classified blocks to accelerate content search and browsing.

  Accordingly, the present invention provides the following block classification technique.

FIG. 14 illustrates another method for classifying pixels in a block according to the present invention. The image 201 is divided into 8 × 8 blocks that do not overlap as described above. As described above, the variance of each pixel is obtained in the variance image 401. Next, the block classification is determined using the pixel having the maximum variance or standard deviation (variance = σ ² and STD = √ (variance)).

  Each block is classified into one of five categories: strong edge 1401, weak edge 1402, strong texture 1403, weak texture 1404, and smooth 1405.

Block Filtering In the case of progressive video, filtering is performed on each block in each frame, and in the case of interlaced video, filtering is performed on the blocks in each field. As shown in FIG. 14, the filter of each block is selected according to the classification of blocks and the classification of adjacent blocks. Further, filtering is adaptive in that the spread parameter of the filter is proportional to the maximum variance or maximum standard deviation, i.e., the larger the maximum standard deviation, the larger the spread parameter.

  Strong edge block 1401: If all eight neighboring blocks 1421 (upper, lower, left, right, upper left, upper right, lower left, lower right) are strong edge blocks, all-pass filtering 1461 is performed to output the block ( 1442) Otherwise, each pixel is filtered by an adaptive fuzzy filter with a spread parameter ξ of 20 (strong smooth fuzzy filtering), and a block is output.

  Weak edge block 1402: Each pixel is subjected to filtering 1431 with an adaptive fuzzy filter by setting the spread parameter ξ to 10 (which is weak smooth fuzzy filtering), and a block is output.

  Strong texture block 1403: When all four adjacent blocks 1441 (upper, lower, left, and right) are strong edge blocks, all-pass filtering is performed. Otherwise, the filtering 1431 is performed by setting the spread parameter ξ to 10 for each pixel by a fuzzy filter.

  Weak texture block 1404: If at least two of the four adjacent blocks 1451 (up, down, left, right) are smooth blocks, the spread parameter ξ is set to 10 by adaptive fuzzy filter for each pixel Then, filtering 1431 is performed.

  Smoothing block 1405: If the block is a smoothing block, an all-pass filter 1461 is applied and the block is output 1442.

  As shown in FIG. 15A, when a strong edge block 1501 is completely surrounded by other strong edge blocks, filtering of the block can be omitted. This is because ringing artifacts do not appear remarkably in the large strong edge region due to the masking effect. Second, small artifacts only appear in smooth regions, so weak edge blocks 1502 need only be filtered when there are adjacent smooth blocks. Filtering in the texture block may be optional and can be selected according to the compression ratio. When the compression ratio is high, for example, when the quantization scale parameter exceeds 40, there is a high possibility that ringing artifacts appear in the restored texture block. Therefore, weak smooth fuzzy filtering should be applied.

  Although the invention has been described by way of examples of preferred embodiments, it is to be understood that various other adaptations and modifications can be made within the spirit and scope of the invention. Accordingly, the purpose of the appended claims is to cover all modifications and variations that fall within the true spirit and scope of the invention.

1 is a block diagram of a method and system for removing artifacts from a restored image according to the present invention. FIG. 6 is a graph of a Gaussian function for evaluating similarity of pixel values according to the present invention and generating fuzzy filter parameters. It is a block diagram of feature extraction according to the present invention. FIG. 6 is a block diagram of mapping between intensity images and variances according to the present invention. FIG. 3 is a block diagram for classifying pixels according to the present invention. FIG. 6 is a block diagram for detecting blocking artifacts according to the present invention. FIG. 6 is a block diagram for filtering blocking artifacts according to the present invention. It is a graph which shows the smoothing characteristic adaptive to the data of the fuzzy filter by this invention. 3 is a flowchart of adaptive filtering according to the present invention. It is a block diagram of detection of a blocking artifact in the vertical direction. FIG. 7 is a block diagram of vertical blocking artifact removal. FIG. 6 is a block diagram of detection of horizontal blocking artifacts. FIG. 6 is a block diagram of removal of lateral blocking artifacts. 3 is a flowchart of pixel classification and filtering according to the present invention. It is a block diagram of an edge block surrounded by eight adjacent edge blocks. It is a block diagram of an edge block surrounded by at least two smooth blocks.

Claims (5)

A method for filtering pixels in an image, comprising:
Dividing a plurality of pixels in an input image into a plurality of blocks;
Identifying an edge block in the plurality of blocks;
Determining the variance of the intensity of each pixel in each edge block, and filtering each pixel in each edge block with a filter that depends on the variance of the pixel;
The filter has a spread parameter ξ, where a is the center value of the intensity range of the pixel and b is the intensity of the pixel to be filtered.

Is used to filter by applying a weighted average of each pixel in the window, with the window size being equal to or smaller than the block size , applying the Gaussian function defined in. Corresponding to the spread parameter ξ, a large variance corresponds to a large window size N and a large spread parameter ξ,
The filtering is
If the variance is in the range of 4 to less than 10, setting the spread parameter ξ to 15 and the window size N to 3;
When said dispersion is in the range of 10 to 22, the spread parameter ξ to 30, and setting the window size N to 5, and otherwise, sets the filter to the all-pass filter A method of filtering pixels in an image including: Identifying a row of pixels that intersects the vertical boundary between two adjacent blocks;
Determining a difference in intensity between pixels on the left and right sides of the vertical boundary to detect a boundary gap;
The method of claim 1, further comprising: filtering pixels in the row only when the boundary gap is detected.   The method of claim 2, wherein the filtering uses a one-dimensional fuzzy filter centered on each pixel of the row to be filtered. Identifying a column of pixels that intersects a lateral boundary between two adjacent blocks;
Determining an intensity difference between pixels above and below the lateral boundary to detect a boundary gap;
The method of claim 1, further comprising: filtering pixels in the column only when the boundary gap is detected.   5. The method of claim 4, wherein the filtering uses a one-dimensional fuzzy filter centered on each pixel of the column to be filtered.

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