Patent Publication Number: US-8542751-B2

Title: Techniques for identifying and reducing block artifacts

Description:
FIELD 
     The subject matter disclosed herein relates generally to techniques for identifying and reducing blocking artifacts. 
     RELATED ART 
     Video compression is employed for storage and transmission of video sequences to increase storage efficiency and to reduce bandwidth used to transmit video. Popular video standards such as MPEG and H.264 employ block-based compression techniques to achieve a reduction of bit-rate. In such techniques, each frame of the video is first partitioned into a set of disjointed blocks of fixed size. Advanced compression schemes are then applied to reduce the number of bits to code each block. An unwanted consequence of compression is the presence of visible blocking artifacts in the decoded video. In some cases, the greater the amount of compression of the video sequence, the stronger the blocking artifacts. 
     De-blocking is a post-processing process whose goal is to reduce or remove blocking artifacts and improve the visual quality of the decoded video. Some existing de-blocking techniques assume that the blocks are of a known size (such as 8×8 pixels) and/or that information about the strength of the blocking artifacts is available from the decoder in the form of quantization parameters. However, in video post-processing, block size and/or quantization parameters may not always be available from the decoder. Moreover, the size of the blocks may not necessarily be fixed and may depend on several factors, including:
         a. The coding method adopted (for example, MPEG or H.264).   b. The scanning method adopted (progressive or interlaced).   c. The scaling applied to the image.   d. Use of motion compensation techniques in video compression, which results in the shifting of block boundaries from their typical positions.       

     Accordingly, such techniques may not perform effective de-blocking of images or videos containing blocks of non-standard sizes or those containing shifted blocks due to motion compensation in compressed video sequences. In some cases, there may be residual blockiness where block artifacts were not detected or the output image may suffer from loss of detail resulting from applying smoothing throughout the image. 
     Various techniques to perform de-blocking are described in the following articles: Hoon Paek, Rin-Chul Kim, and Sang-Uk Lee, “On the POCS-Based Postprocessing Technique to Reduce the Blocking Artifacts in Transform Coded Images,” IEEE Transactions On Circuits And Systems For Video Technology, Vol. 8, No. 3, June 1998, pp. 358-367. 
     Amir Z. Averbuch, Alon Schclar, and David L. Donoho, “Deblocking of Block-Transform Compressed Images Using Weighted Sums of Symmetrically Aligned Pixels,” IEEE Transactions On Image Processing, Vol. 14, No. 2, February 2005, pp. 200-212. 
     Shuanhu Wu, Hong Yan, and Zheng Tan, “An Efficient Wavelet-Based Deblocking Algorithm for Highly Compressed Images,” IEEE Transactions On Circuits And Systems For Video Technology, Vol. 11, No. 11, November 2001. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the present invention are illustrated by way of example, and not by way of limitation, in the drawings and in which like reference numerals refer to similar elements. 
         FIGS. 1 and 2  depict example processes that can be used to detect vertical block-edges in smooth image regions. 
         FIGS. 3 and 4  depict example systems that can be used to identify potential block-edges of vertical and horizontal block edges. 
         FIG. 5  depicts an example system that can be used to perform de-blocking of frames in interlaced video. 
         FIG. 6  depicts an example system in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrase “in one embodiment” or “an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in one or more embodiments. 
     Various embodiments attempt to reduce blocking artifacts in compressed images and videos. Various embodiments estimate the location and strength of the blocking artifacts in each image and attempt to distinguish between blocks in smooth regions of an image and blocks in regions of high detail. Consequently, blockiness can be reduced in compressed images and videos that have smooth or detailed regions or non-uniform sized blocks while at the same time retaining the level of sharpness of the input. 
     Blocking artifacts caused by compression can be modeled as true step-edges in either the horizontal or the vertical directions. The location of blocking artifacts can be determined by searching for such step edges. Accordingly, edges in natural images, which are rarely true steps, may not be incorrectly detected as blocks by this technique. Nonetheless, constraints are placed to attempt to detect true block artifacts with a high degree of confidence. Once the location of the artifacts is known, the strength of blockiness at each location is measured. The strength of blockiness can be used to control the parameters of an adaptive de-blocking filter or used in other ways. 
       FIG. 1  depicts an example process to detect vertical block edges in smooth image regions. A smooth image region can be a region with a slow variation in image from one block to the next. Block  102  includes determining a horizontal gradient of a compressed image. A horizontal gradient can be used to find sudden changes in intensity, where intensity describes brightness of pixels. Y[m,n] is the luminance or intensity value at pixel coordinate (m,n), where m values increase along the −y axis and n values increase along the +x axis. Given a compressed image Y[m,n], the absolute value of the horizontal gradient of Y[m,n] can be determined as follows:
 
 G   H   [m,n]=|Y[m,n]−Y[m,n− 1]|,
 
The horizontal gradient is an absolute value of a difference between adjacent pixels in the same row. The step-size of a block edge at (m,n) is the value of horizontal gradient G H  at (m,n). Block  102  repeats for every pixel coordinate in a row to determine the horizontal gradient for every pixel in a row.
 
     To determine a vertical gradient, an absolute value of a difference between adjacent pixels in the same column is determined. 
     Block  104  commences after the horizontal gradients of all pixels in a row have been determined. Block  104  includes determining a low-pass filtered version of horizontal gradients in the row of the pixel at location [m,n]. Applying a low-pass filter aids in the detection of step-edges that are representative of blocking artifacts. A low-pass filtered version of each pixel in the same row as that of the pixel at location [m,n] can be calculated using: 
                   A   H     ⁡     [     m   ,   n     ]       =       (     1   /     (       2   ⁢   N     +   1     )       )     ⁢       ∑     k   =     -   N       N     ⁢       G   H     ⁡     [     m   ,     n   -   k       ]             ,         
where
 
     2N+1 is the length of the filter kernel in the row of the pixel at location [m,n] and the current pixel is the center of the row. 
     For detection and measurement of horizontal block edges, block  104  can be used except that a low pass filtered version of each pixel in the same column as that of the pixel at location [m,n] is calculated. 
     Block  106  includes determining a pixel-wise change in intensity for pixels in the same row. For example, a pixel-wise ratio of the horizontal gradient to the low-pass filtered version of the horizontal gradient can be determined. The pixel-wise ratio of the horizontal gradient, G H [m,n], to the low-pass filtered version of pixels in a row, A H [m,n], can be determined as follows: 
     
       
         
           
             
               
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     Block  108  includes determining whether the ratio exceeds a threshold. Sudden changes in intensity in an image can be due to block artifacts or changes in textures in an image. It can be proven mathematically that the maximum value of the pixel wise ratio determined in block  106  is 2N+1 and is achieved for true step edges, where N is an integer. A value of N=2 is found to give satisfactory detection of block-edges. Many real world images may contain some amount of noise. In such case, a block-edge can be modeled as a step edge corrupted by additive noise. It can be proven that the value of R v [m,n] for such corrupted edges is lower than the theoretical maximum. Accordingly, instead of identifying the pixels where R v [m,n] attains its maximum value, a search is conducted for pixels where R v [m,n] is greater than some threshold T, where T≦2N+1. These pixels are then identified as potential block edges. Accordingly, if the pixel-wise ratio is greater than a threshold T, then block  110  follows block  108 . If the pixel-wise ratio is less than or equal to a threshold T, then block  109  follows block  108 . 
     In some applications, T can be set to a fixed value specified by a user. Threshold T can be set as a function of the noise level in the image (which is either known or measured by other means). In video applications, T may be adaptively set to a value for each input video frame depending on the noise level in each frame. 
     For images or videos having only compression artifacts, the choice of T involves a trade-off between missed detections (true block artifacts that are not detected) and false alarms (pixels that are false detected as having block artifacts). The higher the value of T, the fewer the false alarms. However, there is a greater chance that true block-edges will be left out. For N=2, a value of T=4 is found to give satisfactory results. The presence of random noise in the compressed image sequence may adversely impact the performance of the blockiness detector. For images or video that contain both compression and random noise, the mean or expected value of R V [m,n] for step-edges of size h is given by: 
     
       
         
           
             
               
                 
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     where σ 2  is the variance of the random noise. 
     Thus, in general the presence of random noise lowers the threshold T that should be used for detection of block edges. Using the above relation, T can be set as a function of the noise level in the image. The noise level itself may either be known or can be measured such as by estimating the standard deviation of the image in flat regions. In video applications, T may be adaptively set to a value for each input video frame depending on the noise level in each frame. 
     Block  109  indicates that the pixel at coordinates m and n do not include a blocking artifact. 
     Block  110  includes selecting block edges whose length meets a threshold length. Amongst the pixels identified by block  108 , it is likely that there will be some isolated false positives. To eliminate these isolated false positives, block  110  performs a consistency check to determine if the adjacent pixels that may include blocking artifacts and are in the same column have a minimum length, CLENGTH. If the adjacent detected pixels have length less than CLENGTH, then these detected pixels are treated as false classifications. In some embodiments, a pixel location (m, n) is classified as a vertical block edge location if the following is true:
 
( m−k+ 1 ,n ),( m−k+ 2 ,n ), . . . ,( m−k+C LENGTH, n ) are all potential block edges for any  kε{ 1,2 , . . . ,C LENGTH}.
 
     For detection and measurement of horizontal block edges, adjacent detected pixels that include blocking artifacts and are in the same row are compared against value CLENGTH. 
     Fixing a value of CLENGTH involves a tradeoff between false positives and missed block-edges. Setting CLENGTH to 1 implies that every potential block-edge detected by block  108  is classified as a true block-edge. Setting CLENGTH to high values may result in some true block-edges not being classified as block-edges. 
     Block  112  forms a set of pixels that potentially have block artifacts (denoted Ω V ). The pixels that pass the tests of blocks  108  and  110  are included in the set of pixels that potentially have block artifacts. 
     A textured image region can include many changes in color and image intensity and block boundary detection may be more difficult in textured image regions than in smooth image regions. In textured regions, block artifacts may not always manifest themselves as step edges and hence may not be detected using the process of  FIG. 1 .  FIG. 2  depicts a process for detection of vertical block-edges in textured image regions. An input to the process of  FIG. 2  is an output from the process of  FIG. 1 , namely pixels identified as potentially having block artifacts. However, the process of  FIG. 1  is used on images regardless of whether images are smooth or textured. 
     Block  202  includes determining a number of pixel locations in each column in which vertical blocking artifacts are present. The pixel locations in which vertical blocking artifacts are present can be provided from block  112  of  FIG. 1 . This number can be normalized using the height of the image/video frame in order to make the subsequent steps resolution-independent. This normalized fraction is denoted as BV[k], where k is the column index, can be used to represent the ratio. For example, if the image is 720×480 pixels, a ratio for each of the 720 columns can be determined. In some cases, BV[k] is determined as:
 
“Number of block artifacts in column”/“length of column”
 
The number of pixel locations in each column in which vertical block artifacts are present can be a number of pixels identified in block  112  of  FIG. 1 . The length of a column can be a number of pixels in a column. For example, if an image is 720×480 pixels, then a length of a column is 480.
 
     Block  204  includes determining whether the ratio for the current column is greater than the ratios of previous and next columns. Block  204  determines if the current column is a spike relative to adjacent columns. A spike is a local maximum. A local maximum represents that column has blocking artifacts. BV[k−1] represents a ratio of the previous column and BV[k+1] represents a ratio of the next column. Block  204  includes determining if BV[k] is larger than both columns BV[k−1] and BV[k+1]. Some pixels of columns BV[k−1] and BV[k+1] could contain pixels that have blocking artifacts. Attempts are made to identify columns that have a greater percentage of pixels that have blocking artifacts as compared to their neighbors. If the ratio for the current column is greater than the ratios of previous and next columns, then block  206  follows block  204 . If the ratio for the current column is not greater than the ratios of previous and next columns, then block  205  follows block  204 . 
     Other types of comparisons can be performed such as whether the ratio for the current column is some percentage greater than the ratios of adjacent columns. 
     Block  206  includes determining whether the current column has a ratio BV[k] that is greater than a threshold TBV. If the threshold is met or exceeded, then the column is considered to include a blocking artifact. 
     If the image has been a priori classified into smooth or textured regions via a segmentation process, then this threshold may be set in a region-adaptive fashion. In the absence of such information, a single threshold may be set for the entire image. Such a global threshold can be determined experimentally or may automatically be assigned some reasonable value such as 0.15 (15%). In some embodiments, for each column whose ratio exceeds a threshold, block  208  follows block  206 . In some embodiments, for each column whose ratio does not exceed a threshold, block  205  follows block  206 . 
     Block  208  aggregates a set of columns whose local ratio of BV[k] exceeds a threshold, TBV. The columns in this set are the ones most likely to correspond to true blocking artifacts. In this set of columns, there will be locations that were not detected as block artifacts by the procedure of  FIG. 1 . These locations will likely be in textured/detailed areas of the image because otherwise the proposed detection technique would have already detected block artifacts in the smooth areas using the process of  FIG. 1 . By the process of  FIG. 2 , not only are the locations of block artifacts known, it is also known whether they occur in a smooth region or in a region with details. 
     For colored images, detection schemes can be applied to each individual color plane. A colored image includes three color planes (RGB, or YUV). The procedure described here applies to a single color plane (say R of RGB, or Y of YUV). The entire process can be repeated for each of the colored planes. 
     Thus, by applying the processes of  FIGS. 1 and 2 , the location and the strength of block-artifacts can be detected in a compressed image with high accuracy. The location and strength of block artifacts can be used to control the strength of an adaptive de-blocking filter to reduce the visibility of block artifacts. The strength of the block artifacts determined can be used to set the strength of de-blocking filtering. A different de-blocking filter can be used for each strength level of block artifacts. Accordingly, adaptive de-blocking filtering can be applied for removing blockiness, where blockiness is present while retaining the level of detail in the input image. 
     The following describes a manner to determine strength of blocking artifacts. If a potential block edge is detected at location (m,n), then the step-size of that block edge is used to classify the edge as STRONG, MEDIUM, or WEAK. The step-size of the block edge at (m,n) can be the value of the gradient G H  at (m,n) (determined in block  102  of  FIG. 1 ). In some embodiments, block strength classification can be: 
                                            If G H [m,n] &lt; LOW_TH             BCLASS[m,n] = WEAK           else if G H [m,n] &lt; MED_TH             BCLASS[m,n] = MEDIUM           else             BCLASS[m,n] = STRONG           end.                        
The default value of LOW_TH is chosen to be 6 and the default value of MED_TH is chosen to be 16. Variable BCLASS stores the strengths of the detected blocks.
 
     The strength of the block artifacts can be used to set the strength of a de-blocking filter. This may be done on a per-image basis by determining the average strength of blockiness in an image, or may be done on a per-pixel basis by using the strength of blockiness at each pixel to adaptively determine the strength of deblocking for that pixel. 
     The average value of the gradient G H [m,n] can be calculated for pixels that have been classified as strong block artifacts (BCLASS[m,n]=STRONG). This average value, which is denoted as VSTRONG_METRIC, represents the average strength of blockiness for pixels that are classified as strong vertical block artifacts. Variable VSTRONG_METRIC can be used in the filtering of strong blocking artifacts. For weak strength artifacts, the value of LOW_TH=6 is used and for medium strength artifacts, the value of MEDIUM_TH=16 is used. 
     A de-blocking filter is to convert a sharp block edge to a smoother ramp edge. If a block artifact is present at location (m,n), then the de-blocking filter can be applied to a neighborhood of pixels at that location of the detected block artifacts. For vertical block artifacts, this neighborhood of pixels is defined as follows:
 
 N   V ( m,n )={( m,n+k ),− F WIDTH≦ k≦F WIDTH}.
 
The default value of FWIDTH is chosen to be four (4), although other values can be used. Similarly, for horizontal block artifacts, a neighborhood of pixels are vertically offset from the pixel of interest.
 
     If the strength of the blocking artifact at (m,n) is given by BCLASS[m,n], then all the points in the neighborhood of pixels N v (m,n) are also classified as having strength BCLASS[m,n]. In the event that a pixel (x, y) belongs to the neighborhood of more than one block artifact, then, the conflict is resolved by giving the higher strength to pixel (x,y). The following pseudo-code can be applied. 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
             
            
               
                   
                   
                 If BCLASS [m 1 ,n 1 ] &gt; BCLASS [m 2 ,n 2 ] 
                   
               
               
                   
                   
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                  BCLASS [x,y] = BCLASS [m 2 ,n 2 ] 
                   
               
               
                   
                   
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     The complete set of points where the vertical de-blocking filter is applied therefore includes the points where a block artifact has been detected, along with the corresponding neighborhoods of such points. Mathematically, 
     
       
         
           
             
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     Removal of blocking artifacts can involve applying a bilateral filter in a neighborhood of points around the pixel locations with detected blocking artifacts. The parameters of the bilateral filter applied at a particular pixel location are adaptively determined by the strength of the blocking artifact present at that location (i.e., weak, medium, or strong). One possible procedure is described next. 
     For each pixel in the set of pixels that potentially has block artifacts (Ω V ), a bilateral filter is applied along the horizontal direction (for a row of pixels) to obtain the output filtered pixel value y o [m,n] using the following relationship: 
                 y   o     ⁡     [     m   ,   n     ]       =           ∑     k   =     -   4       4     ⁢         w   d     ⁡     (     n   ,   k     )       ⁢       w   r     ⁡     (       x   ⁡     [     m   ,   n     ]       ,     x   ⁡     [     m   ,   k     ]         )       ⁢     x   ⁡     [     m   ,   k     ]               ∑     k   =     -   4       4     ⁢         w   d     ⁡     (     n   ,   k     )       ⁢       w   r     ⁡     (       x   ⁡     [     m   ,   n     ]       ,     x   ⁡     [     m   ,   k     ]         )             .           
where,
 
 w   d ( n,k )= e   −(n−k)     2     /2σ     d       2   ,
 
 w   r ( x[m,n]·x[m,k ])= e   −(x[m,n]−x[m,k])     2     /2σ     r       2   .
 
     The amount of smoothing produced by the filter is controlled by the estimated artifact strength. The filter is designed such that step edges of strengths comparable to the estimated artifacts strength are filtered out (thereby reducing blockiness) but edges of greater strength are effectively left intact. 
     It was determined experimentally that σ d =1.67 results in superior visual quality of the filtered output. Other values can be used. 
     Value σ r  can be chosen as follows: 
                                            If (BCLASS[m,n] == WEAK)             σ r  = 6;           else if (BCLASS[m,n] == MEDIUM)             σ r  = 16;           else             σ r  = VSTRONG_METRIC;           end.                        
VSTRONG_METRIC represents the average strength of gradient for pixels that are classified as being strong vertical block artifacts.
 
     The removal of horizontal block artifacts involves the same filtering operations as those for vertical block artifacts, except that 1-D row filtering is replaced by 1-D column filtering. 
     It is known that the block artifacts are most visible in smooth regions and that the presence of textures/fine details masks to a certain degree the presence of blocking artifacts. Thus, based on the information obtained by the above procedure, the strength of a deblocking filter could be made stronger in smooth regions and reduced in textured details. This has the effect of significantly reducing the perception of blockiness while at the same time retaining the level of detail in the image or video. 
     If a de-blocking filter can be used between the decoder and encoder of a transcoder, the quality of the transcoded output be improved for a given compression level/bitrate. 
       FIG. 3  depicts an example system that can be used to identify vertical and horizontal block edges. An input can be a compressed image or frame of video. The image can be compressed according to standards such as any form of MPEG or other image or video compression standard. Elements  302 - 308  can perform vertical block-edge classification. Horizontal gradient block  302  can determine a horizontal gradient to find sudden changes in intensity of a pixel. Horizontal gradient block  302  can perform operations described with regard to block  102  ( FIG. 1 ). Gradient smoothing block  304  can apply low pass filtering on the horizontal gradient. Gradient smoothing block  304  can perform operations described with regard to block  104 . Ratio block  306  can perform operations described with regard to block  106 . Vertical block-edge classifier block  308  can determine whether a column of pixels include a blocking artifact. Vertical block-edge classifier block  308  can perform operations described with regard to blocks  108 ,  110 , and  112 . 
     Similar to vertical block-edge classification in blocks  302 - 308 , respective blocks  312 - 318  perform operations to identify horizontal block-edge classifications for horizontal block edges. Vertical gradient block  312  can determine vertical gradient of a pixel. Gradient smoothing block  314  can apply low pass filtering on a vertical gradient. Ratio block  316  can determine a ratio of the vertical gradient over a low pass filtered version of the gradient in a similar manner to that of block  306 . Horizontal block-edge classifier  318  can determine whether a row of pixels include a blocking artifact by performing operations similar to those described with regard to blocks  108 ,  110 , and  112 . 
       FIG. 4  depicts an example of a system that can identify vertical and horizontal block edges in textured regions. A binary map of vertical block-edges can be input to a vertical block-artifact counter  402 . The map of block-edges determined using the process of  FIG. 1  can be input to block  402 . Vertical block-artifact counter  402  can determine a number of pixels in a column that include blocking artifacts. Vertical block-artifact counter  402  can perform the operations of block  202  of  FIG. 2 . Maxima detector  404  can determine whether the current column represents a local maximum of number of pixels in a column that include blocking artifacts. Maxima detector  404  can perform the operations of block  204  of  FIG. 2 . Textured block-edge classifier  406  can form a set of columns that have blocking artifacts. Columns can be considered to have blocking artifacts if the ratio of number of blocking artifacts in the column to number of pixels in a column exceeds a threshold. Textured block-edge classifier  406  can perform the operations of blocks  206  and  208 . 
     For horizontal block-edges, horizontal block-artifact counter  412  can determine a number of pixels in a row that include blocking artifacts. Maxima detector  414  can determine whether the row that includes blocking artifacts has a local maximum of number of blocking artifacts relative to adjacent rows. Textured block-edge classifier  416  can classify the row as having a blocking artifact if a ratio of number of blocking artifacts in the row to the length of the row exceeds a threshold. 
     In interlaced video, two fields captured at different time instants are woven together to form a single interlaced frame. Interlaced content presents additional challenges to de-blocking.  FIG. 5  depicts an example of a system that can be used to perform de-blocking of frames in interlaced video. Vertical blockiness detection block  452  can detect vertical blocking artifacts for artifacts in a frame with every other row of lines using a process described herein. Every other row of lines can be considered adjacent pixels. Horizontal detection block  454  can detect horizontal blocking artifacts using a process described herein. Vertical de-blocking filter  456  can apply de-blocking filtering based on horizontal gradients after vertical and horizontal blockiness detection. De-blocking filtering can take place based on the strength of blocking artifacts. De-interlacer  458  can perform de-interlacing on videos to produce a merged frame. Horizontal de-blocking filter  460  can perform horizontal de-blocking based on vertical gradients. De-blocking filtering can take place based on the strength of blocking artifacts. De-blocking in textured regions  462  can perform similar filtering as that in a smooth region in that the same type of bilateral filter can be used. The average strength of the blockiness can used to control the strength of the filter for all pixels in textured regions, rather than classifying artifacts as strong, medium and weak as can be done in smooth regions. 
     Filtering of horizontal blocking artifacts involves filtering in the vertical direction. Such an operation may not be performed on an interlaced frame (a frame in which both odd and even fields are interleaved). Filtering can take place on a single field, as opposed to a frame of interleaved fields, but there is a risk of causing excessive smoothing. This is because points that appear one pixel apart in the field are actually two pixels apart in the de-interlaced image. Thus, it may be desirable to perform vertical filtering after the de-interlacing operation. 
     On the other hand, de-interlacing can involve some type of interpolation of the missing field. A result of this is that blocking artifacts that appeared as true step-edges in a field may not be true step-edges after the de-interlacing operation is carried out. This may reduce the accuracy of detection of block artifacts. Thus, it may be preferable to perform detection before the de-interlacing operation. 
       FIG. 6  depicts an example system in accordance with an embodiment. Computer system  500  may include host system  502  and display  522 . Computer system  500  can be implemented in a handheld personal computer, mobile telephone, set top box, or any computing device. Any type of user interface is available such as a keypad, mouse, and/or touch screen. Host system  502  may include chipset  505 , processor  510 , host memory  512 , storage  514 , graphics subsystem  515 , and radio  520 . Chipset  505  may provide intercommunication among processor  510 , host memory  512 , storage  514 , graphics subsystem  515 , and radio  520 . For example, chipset  505  may include a storage adapter (not depicted) capable of providing intercommunication with storage  514 . 
     Processor  510  may be implemented as Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors, x86 instruction set compatible processors, multi-core, or any other microprocessor or central processing unit. 
     Host memory  512  may be implemented as a volatile memory device such as but not limited to a Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), or Static RAM (SRAM). Storage  514  may be implemented as a non-volatile storage device such as but not limited to a magnetic disk drive, optical disk drive, tape drive, an internal storage device, an attached storage device, flash memory, battery backed-up SDRAM (synchronous DRAM), and/or a network accessible storage device. 
     Graphics subsystem  515  may perform processing of images such as still or video for display. An analog or digital interface may be used to communicatively couple graphics subsystem  515  and display  522 . For example, the interface may be any of a High-Definition Multimedia Interface, DisplayPort, wireless HDMI, and/or wireless HD compliant techniques. Graphics subsystem  515  could be integrated into processor  510  or chipset  505 . Graphics subsystem  515  could be a stand-alone card communicatively coupled to chipset  505 . In various embodiments, processor  510  and/or graphics subsystem  515  performs instructions that identify blocking artifacts and potentially correct blocking artifacts based on techniques described herein. 
     Radio  520  may include one or more radios capable of transmitting and receiving signals in accordance with applicable wireless standards such as but not limited to any version of IEEE 802.11 and IEEE 802.16. For example, radio  520  may include at least a physical layer interface and media access controller. 
     The graphics and/or video processing techniques described herein may be implemented in various hardware architectures. For example, graphics and/or video functionality may be integrated within a chipset. Alternatively, a discrete graphics and/or video processor may be used. As still another embodiment, the graphics and/or video functions may be implemented by a general purpose processor, including a multi-core processor. In a further embodiment, the functions may be implemented in a consumer electronics device. 
     Embodiments of the present invention may be implemented as any or a combination of: one or more microchips or integrated circuits interconnected using a motherboard, hardwired logic, software stored by a memory device and executed by a microprocessor, firmware, an application specific integrated circuit (ASIC), and/or a field programmable gate array (FPGA). The term “logic” may include, by way of example, software or hardware and/or combinations of software and hardware. 
     Embodiments of the present invention may be provided, for example, as a computer program product which may include one or more machine-readable media having stored thereon machine-executable instructions that, when executed by one or more machines such as a computer, network of computers, or other electronic devices, may result in the one or more machines carrying out operations in accordance with embodiments of the present invention. A machine-readable medium may include, but is not limited to, floppy diskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), magneto-optical disks, ROMs (Read Only Memories), RAMs (Random Access Memories), EPROMs (Erasable Programmable Read Only Memories), EEPROMs (Electrically Erasable Programmable Read Only Memories), magnetic or optical cards, flash memory, or other type of media/machine-readable medium suitable for storing machine-executable instructions. 
     The drawings and the forgoing description gave examples of the present invention. Although depicted as a number of disparate functional items, those skilled in the art will appreciate that one or more of such elements may well be combined into single functional elements. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of the present invention, however, is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of the invention is at least as broad as given by the following claims.