Abstract:
A method for preventing artifacts in an electronic image decoded from a block-transform coded representation of an image, the method comprises the steps of: receiving blocks of the electronic image decoded from the transform-coded representation of the image; determining whether a portion of the decoded image contains low detail pixels; determining boundary pixels as pixels within a predetermined area of a predetermined number of low detail pixels; filtering the boundary pixels with one of a plurality of directionally-oriented smoothing filters for obtaining one or more boundary replacement pixel values; and reconstructing the image by replacing one or more pixels in the boundary with one or more of the boundary replacement pixel values.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to U.S. Pat. No. 5,737,451 issued Apr. 7, 1998 by Gandhi et al., and entitled, “A Method and Apparatus for Suppressing Blocking Artifacts in Block-Transform Coded Images”. 
    
    
     MICROFICHE APPENDIX 
     The disclosure in the microfiche appendix of this patent disclosure of this patent document contains material to which a claim of copyright protection is made. The copyright owner has no objection to the facsimile reproduction of any one of the patent documents or the patent disclosure, as it appears in the U.S. Patent and Trademark Office patent file or records, but reserves all other rights whatsoever. 
     FIELD OF THE INVENTION 
     The invention relates generally to the field of image processing and, more particularly, to removing artifacts resulting from block-transform coded images. 
     BACKGROUND OF THE INVENTION 
     Various numerically lossy compression methods are used in digital image processing to compress image data prior to transmission from one computer workstation to another computer workstation where the image data is decompressed (i.e., reconstructed). A well-known compression method employs a discrete cosine transform (DCT), such as lossy JPEG (Joint Photographic Expert Group) international standard. 
     In this regard, a digital representation of an image is divided into a plurality of non-overlapping, contiguous 8×8 blocks of pixel data. Each non-overlapping 8×8 pixel block of image data is then transformed, via the DCT, from a pixel representation space into a DCT representation space. Each transformed block of image data is comprised of one DC coefficient and 63 AC coefficients. The DC coefficient represents the average brightness of the block and the AC coefficients represent the spatial frequency information in the block. 
     During the coding process, the DC and AC coefficients for each block are quantized and encoded into a bit-stream prior to any transmission to another computer workstation. Quantization, in effect, introduces numerical loss by mapping a range of coefficient values to one value, which mapping is referred to hereinafter as a quantization level. Encoding assigns a binary code to the resulting set of quantized values. At the receiving workstation, the bit-stream is decoded and dequantized to reconstruct the set of dequantized coefficient values. These dequantized coefficients are subsequently transformed back into the pixel representation space via an inverse discrete cosine transform (IDCT), as is well known in the art. 
     At the compression workstation, the number of bits generated by the compression process corresponds, in part, to the number of quantization levels used in the quantization. Using a fewer number of quantization levels, coarse quantization, will generate a fewer number of bits than a less coarse quantization. However, coarse quantization introduces undesirable artifacts at the decoding workstation. Coarse quantization may increase the disparity between the DC coefficients of neighboring blocks, and it may destroy AC coefficient information within a block. This results in artifacts that are oftentimes visually more objectionable in regions of slowly varying intensity. 
     As used herein, an image region is said to be “low detail” if it contains relatively low spatial frequency information, as is the case in regions of slowly varying intensity. Additionally, an image region is said to be “boundary” if it is near a low detail region. 
     A standard technique for reducing blocking artifacts is known as AC prediction, such as that referenced by Mitchell, J. L. and W. B. Pennebaker “JPEG ENHANCEMENTS”  Still Image Data Compression Standard  1993, page 261-265, and is applied at the decompression workstation. AC prediction for low frequency AC coefficients is formed using dequantized DC coefficients from the current block and its eight nearest neighbor blocks. The AC predicted coefficient values for the block replace the zero-quantized AC coefficient values (i.e., those that have been quantized to zero) prior to transforming the image back into the pixel representation space. One shortcoming of this technique is that the AC predicted coefficients replace the zero-quantized AC coefficient values regardless of the other spatial frequency content of the block. This has the undesirable tendency of visually smoothing out high frequency image detail, and of introducing low frequency AC information which has no effect in reducing the disparity of dequantized DC coefficients between neighboring blocks. 
     An adaptive method for reducing blocking artifacts is disclosed in an European Patent Application 0585573A2 by De Garido et. al. An adaptive AC Predictor is based on a prescribed activity measure of the current image block and its eight nearest neighbor blocks. This technique, unlike the above-described method, does not introduce low frequency information via AC prediction in high spatial frequency areas; thus, high spatial frequency and texture information which is indicative of high activity blocks is preserved. This adaptive AC prediction technique still has the disadvantage of not reducing the disparity of the dequantized DC coefficients between neighboring blocks in low activity image areas. 
     Consequently, a need exists for improvements in the decompression of block-transform coded images so as to overcome the above-described drawbacks. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to overcoming one or more of the problems set forth above. Briefly summarized, according to one aspect of the present invention, the invention resides in a method for preventing artifacts in an electronic image decoded from a block-transform coded representation of an image, the method comprising the steps of: (a) receiving blocks of the electronic image decoded from the transform-coded representation of the image; (b) determining whether a portion of the decoded image contains low detail pixels; (c) determining boundary pixels as pixels within a predetermined area of a predetermined number of low detail pixels; (d) based on step 1(c), filtering the boundary pixels with one of a plurality of directionally-oriented smoothing filters for obtaining one or more boundary replacement pixel values; and (e) reconstructing the image by replacing one or more pixels in the boundary with one or more of the boundary replacement pixel values. 
     These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings. Note also that the description to follow details the operation of the present invention on either a monochrome image or on one channel (the luminance channel) of a color image. The extension of the invention to the other channels (the chrominance channels) of a color image will be described at the end of the detailed description. 
     ADVANTAGEOUS EFFECT OF THE INVENTION 
     The present invention has the following advantage of identifying regions containing low spatial frequency information and boundary regions near them, and of performing a unique smoothing operation according to the identified content. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a perspective drawing illustrating a typical computer system for implementing the present invention; 
     FIG. 2 is a block diagram illustrating an overview of the present invention; 
     FIG. 3 is an exploded block diagram of a portion of FIG. 2 illustrating a block mean smoother; 
     FIG. 4 is a diagram illustrating a 3×3 window used for computing the local variance of FIG. 3; 
     FIG. 5 is an exploded block diagram of a portion of FIG. 2 illustrating gradient computation and pixel labeling; and, 
     FIG. 6 is an exploded block diagram of a portion of FIG. 2 illustrating smoothing of low detail and boundary pixels. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, the present invention will be described in the preferred embodiment as a software program. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. 
     Referring to FIG. 1, there is shown a system for implementing the present invention. A digital camera  10 , such as a “KODAK” DC  50 , includes a charge-coupled device, CCD (not shown), for receiving incident light through a lens  20 , and for converting the incident light into an electronic, digital representation of an image  25  that is contained within the incident light, as is well known in the photographic industry. The CCD transfers the electronic representation of the image to a personal computer card (PCC)  30  for storing the image for later retrieval. 
     However, before storage of the image on the PCC  30 , JPEG compression software which is stored on a memory module (not shown) of the camera  10  is used for compressing the digital representation of the image received from the CCD. Such compression is desirable for limiting the amount of memory necessary for storing the digital representation of the image on the PCC  30 . 
     The PCC  30  is inserted into a local computer or workstation  40  for permitting the representation of the image to be decompressed and viewed on a monitor  50  that is electrically connected to the local computer  40 . Alternatively, the digital representation of the image  25 , which is still in compressed form, may be transmitted to another remote computer or workstation  60  where it is decompressed for viewing on a monitor  70 , printing by a printer (not shown) or further processing by a remote user. A software program of the present invention is stored on the remote computer  50  for processing the decompressed digital representation of the image to reduce the occurrence of artifacts for permitting such viewing. 
     Referring now to FIG. 2, there is shown a graphical illustration of the software program of the present invention for reducing the occurrence of artifacts in the image when it is reconstructed. It is instructive to note that this software, in its preferred embodiment, receives the image in a form that has been compressed by lossy JPEG compressor, and subsequently decompressed by a decoder. The decoder dequantizes the incoming data, and performs an inverse discrete cosine transform operation so that the resulting reconstructed digital image includes 8×8 blocks of pixel values; the decoding operation is well known in the art and will not be discussed in detail herein. 
     Referring now to FIGS. 2 and 3, the reconstructed digital image is input into a block mean smoother  80  that performs an adjustment on the mean value of the image blocks for facilitating the deblocking process. In this regard, the block mean smoother  80  first computes  80   a  the mean pixel value of each 8×8 block, each of these mean pixel values being referred to hereinafter as a mean block value. For purposes of illustration, if the standard JPEG reconstructed image is 512×512 pixels, the low resolution, block mean image will be an array of 64×64 mean pixel values. 
     The local variance of each particular mean block value is then calculated  80   b  using a 3×3 array centered on the particular mean block value, as illustrated in FIG. 4 where n 1-9  represent mean block values  85  with the 3×3 array being centered on mean block value n 5 . In the preferred embodiment, blocks near the edge of the image, so that the block mean array  85  cannot be centered entirely within the image, are left unmodified. Those skilled in the are will realize there are other options for handling these border blocks. Referring back to FIGS. 2 and 3, letting b(m, n) indicate a block mean value, the local variance σ 2   b  (m, n) in the preferred embodiment is given by                    σ   b   2          (     m   ,   n     )       =       1   9            ∑     i   =     m   -   1         m   +   1                         ∑     j   =     n   -   1         n   +   1                         (       b        (     i   ,   j     )       -       b   _          (     m   ,   n     )         )     2             ,           (   1   )                                
     where i, j and m,n indicate the block index within the 3×3 array and the block index within the entire image, respectively, and where the term {overscore (b)} (m, n) is defined by                  b   _          (     m   ,   n     )       =       1   9            ∑     i   =     m   -   1         m   +   1                         ∑     j   =     n   -   1         n   +   1                         b        (     i   ,   j     )       .                   (   2   )                                
     Those skilled in the art will realize that various similar measures of the variance given by Equation (1) can be used with appropriate medications. If the local variance σ 2   b  (m, n) is less than some threshold  80   c , preferably T b =64, then the block mean pixel b(m, n) is replaced by a weighted average of the block mean values in its surrounding 3×3 window of 8×8 block means  80   c . This weighted average is referred to as {circumflex over (b)}(m, n) and it is calculated as follows:                    b   ^          (     m   ,   n     )       =       ∑     i   =     m   -   1         m   +   1                         ∑     j   =     n   -   1         n   +   1                         w        (       m   -   i     ,     n   -   j       )            b        (     i   ,   j     )               ,           (   3   )                                
     where i, j and m, n again indicate the block index within the 3×3 array and the block index within the entire image, respectively, and where the w is a 3×3 weighting window              w   =         1   12                [         1       1       1           1       4       1           1       1       1         ]     .             (   4   )                                
     Other weighting windows can certainly be used if desired as those skilled in the art can determine. An alternative window is as follows:                w   2     =         1   16                [         1       2       1           2       4       2           1       2       1         ]     .             (   5   )                                
     The pixels in the corresponding block of the full resolution image are adjusted  80   d  so that their mean is equal to the adjusted mean {circumflex over (b)}(m, n). 
     An alternative method for the block mean smoothing is as follows: 
     As described above, the mean value of a block is replaced by a local average when the local variance of the block means is less than a threshold. This hard thresholding might not be robust in all cases. It is, therefore, suggested that a slightly modified method for block mean smoothing be used that might prove more robust at the cost of extra computation. Specifically, the block mean b(m, n) is found and then σ 2   b  as in Equation (1) and {circumflex over (b)}(m, n) as in Equation (3) are computed. The block mean b(m, n) is then always replaced by a value {circumflex over (b)}(m, n) given by 
     
       
         {tilde over (b)}( m, n )=α( m, n ) b ( m, n )+(1−α( m,n )){circumflex over (b)}( m, n )  (6) 
       
     
     where                  α        (     m   ,   n     )       =         σ   b   2          (     m   ,   n     )             σ   b   2          (     m   ,   n     )       +     σ   ap   2           ,           (   7   )                                
     and where the parameter σ 2   ap  is defined a priori. As the local variance σ 2   b  (m, n) increases, α(m, n) approaches 1 and {tilde over (b)} (m, n) tends toward b(m, n), leaving the block mean essentially unchanged. When σ 2   b  (m, n) is very small, however, α(m, n) approaches 0 and {tilde over (b)} (m, n) approaches {circumflex over (b)}(m, n). From experimentation, it was found that the results using this alternate method with σ 2   ap =75 were indistinguishable from the results using the hard threshold method described previously. 
     As should be obvious to those skilled in the art, the block mean smoothing can be effectively implemented in the DCT domain, prior to pixel reconstruction. The low resolution image would then be composed simply of the DC coefficients of each block. Since the DC coefficient is 8 times the mean value of the block, the threshold parameter T b  should be scaled by 64. The modified pixels in this low resolution image become the new DC coefficients for the corresponding block prior to reconstruction of the pixel values. 
     Referring to FIGS. 2 and 5, the block mean adjusted image is input into a gradient computation and pixel labeling processor  90  for defining which pixels are low detail, and which pixels are boundary pixels. As will be fully described hereinbelow, the labels of the pixels are used to determine what type of smoothing, if any, is performed on a given pixel. Gradient values  90   a  and  90   b  are calculated for each pixel value as follows. Letting G v  (m, n) indicate the gradient in the v (vertical) direction at position (m, n) and, similarly G h  (m, n) the gradient in the h (horizontal) direction at (m, n), these gradients, for an image f(m, n) are calculated as follows:                  G   v          (     m   ,   n     )       =       1   2                     (       f        (       m   +   1     ,   n     )       -     f        (       m   -   1     ,   n     )         )               (8a)                   G   h          (     m   ,   n     )       =       1   2                       (       f        (     m   ,     n   +   1       )       -     f        (     m   ,     n   -   1       )         )     .               (8b)                                
     These gradient images are stored for later use. Next, a gradient magnitude image, G(m, n) is found  90   c  whose entries are given by 
     
       
           G ( m, n )={square root over ( G   2   v +L ( m, n +L )+ G   2   h +L ( m, n +L ))}.  (9) 
       
     
     This gradient magnitude image is then smoothed  90   c  with a 9×9, separable, Gaussian kernel g whose entries are given by                g        (     m   ,   n     )       =         1     C   g              g   ~          (     m   ,   n     )         =       1     C   g            exp        (     -         (     0.25      m     )     2       2        (   0.3536   )           )            exp        (     -         (     0.25      n     )     2       2        (   0.3536   )           )                   (   10   )                                
     for m, n ε[ 4,−3, . . . , 3,4] and where                C   g     =         ∑     m   =     -   4       4                       ∑     n   =     -   4       4                       g   ~          (     m   ,   n     )           =     31.63      ∠               (   11   )                                
     is a normalization factor. For reference, the separable 1-D coefficients of this kernel are            1     31.634            [     0.243   ,   0.451   ,   0.702   ,   0.915   ,   1.00   ,   0.915   ,   0.702   ,   0.451   ,   0.243     ]       .                          
     The resulting smoothed image will hereinafter be referred to as the smoothed gradient image, G s  (m, n). The smoothing reduces the noise in the gradient estimates of Equation (8) and additionally serves to diffuse large gradients so that nearby pixels are not subsequently labeled low detail, which will be described hereinbelow. A pixel in the block mean adjusted image at position (m, n) is labeled low detail  90   d  if G s  (m, n)≦T g , where T g  is a threshold, preferably 4.5521, although those skilled in the art may alter this threshold depending on the desired result. If G s  (m, n)&gt;T g , the pixel at (m, n) is left unlabeled. This labeling results in a binary image, referred to hereinafter as the “low detail map” where a ‘1’ indicates the corresponding pixel is a low detail pixel and a ‘0’ indicates the corresponding pixel is a boundary pixel. 
     Boundary pixels are identified from the low detail map as follows. A 19×19 square window centered on every ‘0’ in the low detail map (i.e., those pixels not labeled low detail) is preferably used. If this window covers more than one low detail pixel (more than a single ‘1’ in the low detail map), the pixel on which the window is centered is labeled a boundary pixel  90   e . The result is a binary image where a ‘1’ indicates that the corresponding pixel is a boundary pixel and where all other pixels are labeled a ‘0’. The resulting map is referred to hereinafter as the “boundary pixel map.” At this point, all low detail pixels are indicated by a ‘1’ in the low detail map, all boundary pixels are indicated by a ‘1’ in the boundary pixel map, and all other pixels are left unlabeled. Other means of storing these pixel labels should be evident to those skilled in the art. 
     Referring to FIGS. 2 and 6, pixels in the block mean adjusted image are smoothed  100  according to their labels obtained from the gradient computation and pixel labeling processor  90 . The boundary pixels are smoothed  100   a  using one of four 5×5 directionally oriented filters whose kernels are given below:                d   1     =       1   108          [         0       1       4       1       0           0       4       16       4       0           0       8       32       8       0           0       4       16       4       0           0       1       4       1       0         ]               (   12   )                 d   2     =       1   108          [         0       0       0       0       0           1       4       8       4       1           4       16       32       16       4           1       4       8       4       1           0       0       0       0       0         ]               (   13   )                 d   3     =       1   128          [         4       4       1       0       0           4       16       8       2       0           1       8       32       8       1           0       2       8       16       4           0       0       1       4       4         ]               (   14   )                 d   4     =       1   128          [         0       0       1       4       4           0       2       8       16       4           1       8       32       8       1           4       16       8       2       0           4       4       1       0       0         ]               (   15   )                                
     It is noted that different kernels can be used if desired. Kernels oriented in more directions can also be employed with modifications to the decision process described below. This directional smoothing of the boundary pixels tends to preserve the integrity of edges while reducing edge artifacts such as ringing and/or staircasing. 
     For a boundary pixel at position (m, n), the kernel used for smoothing is determined from the gradients G v  (m, n) and G h  (m, n) using the following decision process: 
     if||G v (m, n)|−|G h (m, n)||&lt;T dir then 
     if sign [G v (m, n)]=sign[G h (m, n)], then use d 3    
     else, use d 4    
     else if|G v (m, n)|&lt;|G h (m, n)|, then use d 1    
     4. else, use d 2 . 
     The effect of this decision process is as follows. If the vertical and horizontal gradients are approximately equal in magnitude, smoothing is performed in one of the diagonal directions (i.e., d 3  or d 4 ), according the signs of the two gradients. Otherwise, if the magnitude of the gradient in the vertical direction is smaller than that in the horizontal direction, smoothing is done in the vertical direction (i.e., d 1 ). Otherwise (i.e., the magnitude of the gradient in the horizontal direction is smaller than that in the vertical direction) smoothing is done in the horizontal direction (i.e., d 2 ). 
     Preferably, T dir  is 5. It facilitates understanding to note that the particular filter used depends upon the slope of the edge of the boundary. The low detail pixels are smoothed  100   b  with a large kernel, lowpass filter. In our experiments, we employ an 11×11, separable, Gaussian kernel l with entries                l                   (     m   ,   n     )       =         1     C   l                       l   ~                     (     m   ,   n     )       =       1     C   l                     exp                   (     -         (     0.2      m     )     2       2                   (   0.1880   )           )                   exp                   (     -         (     0.2      n     )     2       2                   (   0.1880   )           )                 (   16   )                                
     for m, nε[−5−4, . . . , 4,5] and where                C   l     =         ∑     m   =     -   5       5                       ∑     n   =     -   5       5                       l   ~                     (     m   ,   n     )           =   28.915             (   17   )                                
     is a normalization factor. For reference, the separable 1-D coefficients of this kernel are          1     28.915       ×            [     0.070   ,   0.182   ,   0.384   ,   0.653   ,   0.899   ,   1.00   ,   0.899   ,   0.653   ,   0.384   ,   0.182   ,   0.070     ]     .                              
     The 11×11 size is chosen so that the kernel always extends significantly across block boundaries, thereby decreasing blocking effects. Again it is noted that other kernels can be employed, as those skilled in the art should find apparent. 
     The remaining unlabeled pixels are left unchanged. This serves to preserve sharpness in areas where artifacts are masked by texture or other detail. The output of the pixel smoothing stage  100  is the final processed image. 
     A pseduocode program for performing the steps of the present invention (on a monochrome image or the luminance channel of a color image) is contained in appendix A. 
     To this point, as mentioned previously, the operation of the present invention has been described for a monochrome image or for a single (luminance) channel of a color image. In the case of color images, the operation of the present invention on the other (chrominance) channels is as follows. One simple, computationally efficient solution would be to apply the current invention to only the luminance channel and leave the other chrominance channels unchanged. Another straightforward implementation would be to apply the current invention on first the luminance channel, as has been described, and then to repeat on each of the other chrominance channels separately. In the preferred embodiment, however, the current invention is applied first to the luminance channel and the resulting information is used to apply the invention to the remaining chrominance channels. as follows. If the mean of a given block in the luminance channel is adjusted  80   d , the means of the corresponding blocks in the chrominance channels are adjusted as well. The pixel labels from the luminance channel (i.e., the low detail map and the boundary pixel map) are used to label the corresponding pixels in the chrominance channels. The directions selected for filtering the boundary pixels in the luminance channel are used to determine the direction for smoothing the boundary pixels in the chrominance channels. Finally, according to this information from the luminance channel, the low detail and boundary pixels in the chrominance channels are smoothed appropriately. 
     The invention has been described with reference to a preferred embodiment. However, it will be appreciated that variations and modifications can be effected by a person of ordinary skill in the art without departing from the scope of the invention. 
     APPENDIX 
     FOR each block (m, n) 
     compute mean of block (m, n) 
     compute mean of the other 8 blocks in 3×3 array of blocks centered on block (m, n) 
     compute variance of these 9 block means\\ Equation (1), p. 8 
     IF variance less than Tb 
     \\ Tb=64, p. 8 
     compute weighted average of the block means\\ Equation (3), p. 8 
     modify block (m, n) so that its mean is this weighted average 
     ELSE 
     leave block (m, n) unchanged 
     END IF \\ Result is block_mean_adjusted_image 
     END FOR 
     FOR each pixel (i, j) in block_mean_adjusted_image 
     compute v gradient at (i, j) and store in v_gradient_image\\ Equation (8 a ), p. 11 
     compute h gradient at (i, j) and store in h_gradient_image\\ Equation (8 b ), p. 11 
     compute gradient magnitude at (i, j) and store in gradient_magnitude_image 
     \\ Equation (9), p. 11 
     END FOR 
     initialize label_map to all ‘0s’ 
     FOR each pixel (i, j) in gradient_magnitude_image 
     compute smoothed gradient magnitude at (i, j) 
     \\ smoothing kernel given by Equation (10), p. 11 
     IF smoothed gradient magnitude at (i, j) less than Tg 
     \\ Tg=144, p. 12 
     label pixel (i, j) low detail and store ‘L’ in label_map at (i, j) 
     label all surrounding pixels boundary (unless already labeled low detail) 
     and store ‘Bs’ appropriately in label_map (where there aren&#39;t ‘Ls’) 
     ELSE 
     leave pixel (i, j) unlabeled and leave label_map (i, j) unchanged 
     END IF 
     END FOR 
     FOR each pixel (i, j) in block_mean_adjusted_image 
     IF (i, j) in label_map is ‘L’ (i.e., if low detail pixel) 
     compute low detail smoothed pixel and store in final_image at (i, j) 
     \\ kernel given by Equations (15) and (16), p. 14 
     ELSE IF (i, j) in label_map is ‘B’ (i.e., if boundary pixel) 
     decide direction for smoothing 
     based on (i, j) in x_gradient_image and y_gradient_image\\ see p. 14 
     smooth in chosen direction with appropriate kernel 
     and store in final_image at (i, j) 
     \\ directional smoothing kernels given in Equations (11)-(14), p. 13 
     ELSE (i.e., unlabeled pixel) 
     take value of pixel (i, j) in block_mean adjusted_image and store at (i, j) 
     in final_image 
     END IF 
     END FOR