Abstract:
A method of encoding a video sequence including a sequence of video images includes comparing elements of a portion of a first video image with elements of a portion of a second video image to generate respective intensity difference values for the element comparisons. Then, a first value is assigned to the intensity difference values that are at least above a visually perceptible threshold value and a second value is assigned to the intensity difference values that are not at least above the visually perceptible threshold value. Next, the method includes dividing the portion of the first video image into sub-portions and summing the first and second values associated with each corresponding sub-portion to generate respective sums. If a respective sum is at least greater than a decision value, a variable associated with that sub-portion is set to a first value. If a respective sum is not at least greater than the decision value, the variable associated with that sub-portion is set to a second value. The values associated with the variables are then added. Depending on the result of the addition, the portion of the first video image is either motion compensated or not.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is related to the U.S. patent application identified as Ser. No. 09/239,135, entitled “Perceptual-based Spatio-Temporal Segmentation For Motion Estimation,” and filed concurrently herewith on Jan. 28, 1999. 
    
    
     FIELD OF THE INVENTION 
     The invention relates generally to video compression techniques and, more particularly, to perceptual-based video signal coding techniques resulting in reduced complexity of video coders implementing same. 
     BACKGROUND OF THE INVENTION 
     The age of digital video communications is arriving slower than many had anticipated. Picturephone (1970s) and videophone (1990s) were not commercial successes because they did not provide full color, full motion video and were not cost effective. Desktop video, using windows in computer monitors or TV screens, requires special purpose chips or graphics accelerators to perform the encoding operations. The chips usually come mounted on video boards that are expensive and whose installation intimidates most users. One main reason that these video processors are necessary is attributable to the use of block-based motion compensation, although two-dimensional block-based transforms and lossless compression of quantized transform coefficients add to the computational burden. Motion compensation accounts for over 60% of the computational effort in most video compression algorithms. Although there are algorithms that avoid motion compensation, such as Motion-JPEG, they tend to consume some ten times more transmission bandwidth or storage space because they fail to capitalize on the interframe correlation between successive frames of video. This is especially critical in video conferencing and distance learning applications, thus, rendering these algorithms uncompetitive in such applications. 
     Sources such as speech and video are highly correlated from sample to sample. This correlation can be used to predict each sample based on a previously reconstructed sample, and then encode the difference between the predicted value and the current sample. A main objective of motion compensation is to reduce redundancy between the adjacent pictures. There are two kinds of redundancy well known in video compression: (i) spatial (intra-frame) redundancy; and (ii) temporal (inter-frame) redundancy. Temporal correlation usually can be reduced significantly via forward, backward, or interpolative prediction based on motion compensation. The remaining spatial correlation in the temporal prediction error images can be reduced via transform coding. In addition to spatial and temporal redundancies, perceptual redundancy has begun to be considered in video processing technology, e.g., N. S. Jayant et al., “Signal Compression Based on Models of Human Perception,” Proceedings of IEEE, Volume 10, October 1993. 
     FIG. 1 illustrates a block diagram of a widely used video encoder  10  for encoding video signals for transmission, storage, and/or further processing. The encoder  10  includes a motion estimator  12  and a signal subtractor  14 , both coupled to the input of the encoder. The encoder  10  also includes a transformer (e.g., a discrete cosine transform or DCT generator)  16  coupled to the signal subtractor  14 , a quantizer  18  coupled to the transformer  16 , and an entropy encoder  20  coupled to the quantizer  18  and the output of the encoder  10 . An inverse transformer (e.g., an inverse DCT generator)  22  is also included and coupled between the quantizer  18  and the entropy encoder  20 . The encoder  10  also includes a signal combiner  24  coupled to the inverse transformer  22 , a delay  26  coupled to the signal combiner  24 , and a motion compensator  28  coupled to the delay  26 , the signal subtractor  14 , the signal combiner  24 , and the motion estimator  12 . Also included in the encoder  10  is a rate control processor  30  coupled to the quantizer  18 . 
     It is known that motion estimation and motion compensation, as described in detail in Y. Nakaya et al., “Motion Compensation Based on Spatial Transformations,” IEEE Transactions on Circuits and Systems for Video Technology,” Volume 4, Number 3, Pages 339-356, June 1994, can be used to improve the inter-frame prediction by exploiting the temporal redundancy in a sequence of frames. The motion estimator  12  performs n×n (n typically equals 16) block-based matching of the k th  input frame F k  using the k−1 st  decompressed frame {circumflex over (F)} k−1  (generated by delay  26 ) as the reference. The matching criterion usually employed is mean absolute error (MAE), although mean square error (MSE) may alternatively be employed. For the i th  macroblock, the error measure en i (d) for the displacement vector d between F k  and {circumflex over (F)} k−1  is:                  en   i          (   d   )       =       ∑       (     x   ,   y     )        ε                 B                                F   k          (     x   ,   y     )       -         F   ^       k   -   1            (       x   -   d     ,     y   -   d       )                        (   1   )                                
     where B is the measurement block being predicted. It is evident that a motion vector obtained based on MSE is ∥x∥=x 2  and a motion vector obtained based on MAE is ∥x∥=|x| in equation (1). MAE is usually used, rather than MSE, because MAE is free of multiplications and provides similar results in terms of predictive error. The offset between each block in F k  and the block in {circumflex over (F)} k−1  that best matches it is called the motion vector for that block. That is, the motion vector mv i  for macroblock i is:                mv   i     =     arg          min     d                 ε                 S              en   i          (   d   )                   (   2   )                                
     where S is the search area. Interpolation schemes allow the motion vectors to achieve fractional-pel accuracy, as described in ITU-T Recommendation H.263, “Video Coding For Low Bit Rate Communication,” December 1995. Motion estimation is computationally demanding in that both signals, F k  and {circumflex over (F)} k−1 , entering the motion estimator  12  are high rate and, thus, the operations that have to be performed on them are computationally intensive even if the search for the best-matching block is performed only hierarchically rather than exhaustively. The result of the motion estimation is the set of motion vectors M k  for k th  frame. 
     The M k  are usually losslessly compressed and then conveyed to the transmission channel for immediate or eventual access by the decoder. Also, the M k ′ are fed back to the motion compensator  28  in the prediction loop of the encoder. The M k  constitute a recipe for building a complete frame, herein referred to as {tilde over (F)} k , by translating the blocks of {circumflex over (F)} k−1 . The motion compensated frame {tilde over (F)} k  is subtracted pixel-wise from the current input frame F k , in signal subtractor  14 , to produce a difference frame D k , often referred to as the displaced frame difference (DFD), as further described in T. Ebrahimi et al., “New Trends in Very Low Bit Rate Video Coding,” Proceedings of the IEEE, Volume 83, Number 6, Pages 877-891, June 1995; and W. P. Li et al., “Vector-based Signal Processing and Quantization For Image and Video Compression,” Proceedings of the IEEE, Volume 83, Number 2, Pages 317-335, February 1995. The remaining spatial correlation in D k  is eliminated by the transformer  16  and the quantizer  18 . The transformer may, for example, be a discrete cosine transform (DCT) generator which generates DCT coefficients for macroblocks of frames. The quantizer then quantizes these coefficients. A conventional video encoder such as that shown in FIG. 1 generally attempts to match the bit rate of the compressed video stream to a desired transmission bandwidth. The quantization parameter (QP) used in the quantizer  18  generally has a substantial effect on the resultant bit rate: a large QP performs coarse quantization, reducing the bit rate and the resulting video quality, while a small QP performs finer quantization, which leads to a higher bit rate and higher resulting image quality. The rate control processor  30  thus attempts to find a QP that is high enough to restrain the bit rate, but with the best possible resulting image quality. In general, it is desirable to maintain consistent image quality throughout a video sequence, rather than having the image quality vary widely from frame to frame. Both the MPEG-2 simulation model and the H.263 test model suggest rate control techniques for selecting the QP, however, there are other rate control techniques known to those of ordinary skill in the art. 
     Next, the lossy version of D k , denoted as {circumflex over (D)} k  and generated by inverse transformer  22 , and the motion compensated frame {tilde over (F)} k  are used in the compressor/feedback loop to reconstruct the reference frame {circumflex over (F)} k  for the next input frame F k+1 . Finally, the Huffman (or arithmetic) coded lossy compressed version of D k , generated by the entropy encoder  20 , is transmitted to the decoder. It is to be appreciated that FIG. 1 represents a generic coder architecture described in the current video codec (coder/decoder) standards of H.261, H.263, MPEG-1, and MPEG-2. Further details on these standards are respectively described in: M. Liou, “Overview of the P*64 Kbit/s Video Coding Standard,” Communications of the ACM, Volume 34, Number 4, Pages 59-63, April 1991; ITU-T Recommendation H.263, “Video Coding For Low Bit Rate Communication,” December 1995; D. LeGall, “MPEG: A Video Compression Standard for Multimedia Applications,” Communications of the ACM, Volume 34, Number 4, April 1991; and B. Haskell et al., “Digital Video: An Introduction to MPEG-2,” Chapman and Hall, 1997. 
     Picture quality, coding bit rate, computational complexity, and latency are the four aspects of video codecs that can be traded-off when designing a video codec system. This is further discussed in N. S. Jayant, “Signal Compression: Technology Targets and Research Direction,” IEEE Journal on Selected Areas in Communications, Volume 10, Number 5, Pages 796-818, June 1992. A main objective of a video codec is to represent the original signal with minimal bit rate while maintaining acceptable picture quality, delay, and computational complexity. From the above-mentioned rationale of motion estimation, the motion vectors are attained as the displacement having the minimal error metric. Although this achieves minimum-MAE in the residual block, it does not necessarily result in the best perceptual quality since MAE is not always a good indicator of video quality. In low bit rate video coding, the overhead in sending the motion vectors becomes a significant proportion of the total data rate. The minimum-MAE motion vector may not achieve the minimum joint entropy for coding the residual block and motion vector, and thus may not achieve the best compression efficiency. Another problem occurs in the smooth, still motion backgrounds where zero-displaced motion vectors may not be selected based strictly on minimum-MAE criteria. In this case, the zero-displaced motion vector is a better candidate than the minimum-MAE motion vector because the codeword for the zero-displaced motion vector is usually smaller and, thus, the zero-displaced motion vector will generate lower combined data rates for DCT coefficients and motion vectors without any loss of picture quality. If it can be determined that zero-displaced motion vector is the suitable one to select in the beginning phase, a large computational effort can be saved by avoiding motion estimation for these macroblocks. 
     However, it is to be appreciated that since motion estimation/compensation imposes such a significant computational load on the resources of an encoder and a corresponding decoder, it would be highly desirable to develop encoding techniques that segment frames into portions that should be motion compressed and those that do not need to be motion compressed. 
     SUMMARY OF THE INVENTION 
     The invention provides video encoding apparatus and methodologies which improve the computational efficiency and compression ratio associated with encoding a video signal. This is accomplished by providing perceptual preprocessing in a video encoder that takes advantage of the insensitivity of the human visual system (HVS) to mild changes in pixel intensity in order to segment video into regions according to perceptibility of picture changes. Then, the regional bit rate and complexity is reduced by repeating regions which have changed an imperceptible amount from the preceding frame. In addition, the invention accurately models the motion in areas with perceptually significant differences to improve the coding quality. Depending on picture content, perceptual preprocessing achieves varied degrees of improvement in computational complexity and compression ratio without loss of perceived picture quality. 
     In an illustrative embodiment of the invention, a method of encoding a video sequence including a sequence of video images is provided. The inventive method includes comparing elements of a portion of a first video image (e.g., pixels of a macroblock of a current frame) with elements of a portion of a second video image (e.g., corresponding pixels of a macroblock of a previous frame) to generate respective intensity difference values for the element comparisons. Then, a first value is assigned to the intensity difference values that are at least above a visually perceptible threshold value and a second value is assigned to the intensity difference values that are not at least above the visually perceptible threshold value. In one embodiment of the invention, the visually perceptible threshold value is a function of a quantization parameter associated with the bit rate of the encoding process such that quality-adaptive thresholding is realized. Next, the method includes dividing the portion of the first video image into sub-portions (e.g., four 4×4 blocks of an 8×8 macroblock) and summing the first and second values associated with each corresponding sub-portion to generate respective sums. If a respective sum is at least greater than a decision value, a variable associated with that sub-portion is set to a first value. If a respective sum is not at least greater than the decision value, the variable associated with that sub-portion is set to a second value. The values associated with the variables are then added. Depending on the result of the addition, the portion of the first video image is either motion compensated or not. 
     It is to be appreciated that the present invention takes advantage of the realization that an intensity difference between an isolated pixel or pixels of succeeding video frames, which is at least greater than some visually perceptible threshold value, is nonetheless difficult to detect by the HVS. That is, despite the fact that there may be a number of pixels in a macroblock in a current frame that, when compared to corresponding pixels in a previous frame, result in an intensity difference value at least greater than the visually perceptible threshold value, the macroblock may still not need to be motion compensated and can merely be repeated at the decoder if such individual pixels are isolated from one another in the macroblock. Advantageously, the evaluation of thresholding results with respect to sub-blocks of a macroblock permit a determination as to where the individual pixels of interest are located within the macroblock. In this manner, the present invention implements a randomness-adaptive decision process with respect to deciding whether or not to encode a macroblock. 
     It is to be further appreciated that the invention is fully compatible and, thus, may be implemented with video standards such as, for example, H.261, H.263, Motion-JPEG, MPEG-1, and MPEG-2. 
     These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a block diagram of a conventional video encoder; 
     FIG. 2 is a block diagram of a video encoder according to an embodiment of the present invention; 
     FIG. 3 is a flow chart of an illustrative embodiment of a perceptual preprocessing method according to the present invention; 
     FIG. 4 is a flow chart of another illustrative embodiment of a perceptual preprocessing method according to the present invention; and 
     FIGS. 5A through 5C are tabular representations of simulation results associated with perceptual preprocessing according to the invention. 
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     The following description will illustrate the invention using an exemplary video encoding system. It should be understood, however, that the invention is not limited to use with any particular type of video signal format, video encoding standard or encoding system configuration. The invention is instead more generally applicable to any video encoding system in which it is desirable to improve the efficiency of an encoding process for generating encoded bit streams with respect to computational complexity and compression ratio without adversely affecting perceived picture quality. The term “video sequence” should be understood to include any frame or field sequence which is in a form suitable for encoding in accordance with standards such as, for example, H.261, H.263, Motion-JPEG, MPEG-1 and MPEG-2. The terms “image” or “picture” as used herein refer to a frame or field of a video sequence. The term “block” as used herein is intended to include not only macroblocks as defined in the above-noted compression standards, but more generally any grouping of pixel elements in a video frame or field. 
     Prior to a detailed description of preferred embodiments of the invention, some background on perceptual coding will be given. The history of perceptual coding can be traced back to the Weber-Fechner law of experimental psychology. It was found that the human capacity to distinguish a change in magnitude of a stimulus depends on the current magnitude of that stimulus. For example, it is generally easier to distinguish a 2-pound weight from a 1 -pound weight, than it is to distinguish a 21-pound weight from a 20-pound weight. In the 1800s, Weber developed a quantitative description of the relationship between stimulus intensity and discrimination now known as Weber&#39;s law, namely: 
     
       
         ΔS=K·S  (3) 
       
     
     where ΔS is the perceived intensity difference relative to a background stimulus S and K is a constant. Fechner applied Weber&#39;s law to sensory experience in the 1860s and found that the intensity of a sensation is proportional to the logarithm of the strength of the stimulus: 
     
       
         I=K log (S/T)  (4) 
       
     
     where I is the subjective experienced intensity, T is the threshold, or minimally perceptible stimulus level, and S (&gt;T) is the strength of the supra-threshold stimulus. This relationship is referred to as the Weber-Fechner (WF) law. Further discussion of the WF law may be found in E. G. Boring, “Sensation and Perception in the History of Experimental Psychology,” New York: Appleton-Century 1942. 
     In video, let I denote the actual intensity at a target pixel that is to be encoded, and let I′ denote the displayed intensity at a spatio-temporal immediate neighbor of the target pixel as reconstructed from the data already transmitted. Then, provided |I-I′| is less than the WF threshold T. WF law implies that most human observers will not be able to sense the difference between displaying I′ instead of I at the target pixel. In view of this limitation of human visual perception, we should expect a reasonable perceptual metric for video coding to achieve zero distortion numerically even though some (I, I′) pairs are not exactly the same. A perceptual metric d(z) based on the Weber-Fechner logarithmical relationship has been presented in Y. J. Chiu et al., “Perceptual Rate-Distortion Theory,” Proceedings of the 1996 IEEE International Symposium on Information Theory and Its Applications, Victoria, Canada, Sep. 17-20, 1996. The perceptual metric is represented as:                d        (   z   )       =     {           0   ,             when                      z          ≤   T                 K                   log        (          z        /   T     )         ,             when                      z          ≥   T                     (   5   )                                
     where z=u−v represents the distortion of the input source U approximated by output source V. Recent psychology experiments have shown that the determination of T is very complicated for video because video involves temporal masking and spatial masking at the same time. This is discussed in A. N. Netravali et al., “Digital Pictures: Representation, Compression, and Standards,” 2 nd  Edition, AT&amp;T Bell Laboratories, 1995. The threshold T is sometimes referred to as the “just-noticeable-distortion” (JND) in literature on perception, e.g., Y. J. Chiu et al., “Perceptual Rate-Distortion Theory,” Proceedings of the 1996 IEEE International Symposium on Information Theory and Its Applications, Victoria, Canada, Sep. 17-20, 1996; and N. S. Jayant et al., “Signal Compression Based on Models of Human Perception,” Proceedings of IEEE, Volume 10, October 1993. The ideal JND provides each pixel being coded with a threshold level below which discrepancies are perceptually distortion-free. The minimum rate required for perceptual lossless compression is called “perceptual entropy.” Also of interest is the analysis of noticeable, above-threshold distortions that inevitably result at low bit rates. A term called “maximally noticeable distortion” (MND) characterizes distortion profiles above the perception threshold. In such scenarios, information-theoretic considerations from rate-distortion theory are coupled with research on perceptual metrics in order to adjust thresholds locally in an optimal fashion. A framework of rate-distortion consideration on perceptual metric can be found in Y. J. Chiu et al., “Perceptual Rate-Distortion Theory,” Proceedings of the 1996 IEEE International Symposium on Information Theory and Its Applications, Victoria, Canada, Sep. 17-20, 1996. 
     In accordance with Weber-Fechner theory, the previous displayed frame can be treated as the perceived stimulus background. This permits an update of those pixel sites whose intensity differences exceed the visibility threshold specified by the background pixels. As described above, JND provides each signal being coded with a threshold level below which reconstruction error is imperceptible in the sense that a human can not perceive any impairment in the video if the discrepancies are below the JND value. The information for updating such pixels can be considered to be perceptual redundancy. Perceptual redundancy may be removed in the pixel domain via a procedure referred to as “bush-hogging,” as described below and in U.S. Pat. No. 5,740,278, issued to T. Berger and Y. J. Chiu et al. on Apr. 14, 1998, entitled: “Facsimile-Based Video Compression Method And System,” the disclosure of which is incorporated by reference herein. 
     Although the same threshold value can be used for each pixel, perceptual considerations suggest that only those prediction residuals that exceed a perceptually predetermined threshold need to be corrected. The selection of this perceptual threshold should be based not only on the difference intensity values in the difference frame domain but also on the intensity values of the background pixels. 
     The correct choice of the bush-hog threshold T for a particular pixel depends on many factors. If only achromatic images are considered, the principal factors are: (i) average spatial and temporal background luminance level against which the pixel is presented; (ii) supra-threshold luminance changes adjacent in space and time to the test pixel; and (iii) spatio-temporal edge effects. These factors are further discussed in A. N. Netravali et al., “Digital Pictures: Representation, Compression, and Standards,” 2 nd  Edition, AT&amp;T Bell Laboratories, 1995. In real video, the three factors are correlated with one another. A complete study on the determination of pixel-by-pixel threshold T based on the intensity values of the background pixels and local masking effects is described in detail in the above-referenced U.S. Pat. No. 5,740,278 to Berger et al. 
     Referring now to FIG. 2, an exemplary video encoder  100  in accordance with an illustrative embodiment of the invention is shown. As is evident, the exemplary configuration of the video encoder  100  includes similar components as the video encoder  10  (FIG.  1 ), with the important exception of the novel inclusion of a perceptual preprocessor  110  in the encoder  100  of the invention. As such, a description of the functions of the similar components of FIG. 2 (with their reference designations incremented by  100  as compared to FIG. 1) will not be repeated as they were explained in detail above and are known in the art. As shown, the video encoder  100  includes the perceptual preprocessor  110  coupled to the input of the encoder. The encoder  100  also includes a motion estimator  112  and a signal subtractor  114 , both coupled to the input of the encoder. The motion estimator  112  is also coupled to the perceptual preprocessor  110 . The encoder  100  also includes a transformer (e.g., DCT generator)  116  coupled to the perceptual preprocessor  110  and the signal subtractor  114 , a quantizer  118  coupled to the transformer  116 , and an entropy encoder  120  coupled to the quantizer  118  and the output of the encoder  100 . An inverse transformer (e.g., an inverse DCT generator)  122  is also included and coupled between the quantizer  118  and the entropy encoder  120 . The encoder  100  also includes a signal combiner  124  coupled to the inverse transformer  122 , a delay  126  coupled to the signal combiner  124  and the perceptual preprocessor  110 , and a motion compensator  128  coupled to the delay  126 , the signal subtractor  114 , the signal combiner  124 , and the motion estimator  112 . Also included in the encoder  100  is a rate control processor  130  coupled to the quantizer  118 . 
     From implication of the Weber-Fechner theory, we note that the values of residuals that do not exceed the perceptual threshold T may not need to be corrected. If the residuals of the pixels inside a macroblock are small enough, then we can continue to use the previously displayed macroblock without updating and at no loss of perceived quality. Accordingly, the computational effort of motion estimation/compensation for such macroblock is saved or can be preserved for other macroblocks requiring more motion searches. 
     It is to be appreciated that the perceptual preprocessor  110  of the invention provides such advantages by making use of the insensitivity of the human visual system (HVS) to the mild changes in pixel intensity in order to segment video into regions according to perceptibility of picture changes. With the information of segmentation, we then determine which macroblocks require motion estimation. Thus, as shown in FIG. 2, the current picture (i.e., frame F k ) and the previously reconstructed picture (i.e., frame {circumflex over (F)} k−1 ) are input to the perceptual preprocessor  110 . The following description is an explanation of an illustrative embodiment of a perceptual preprocessing method according to the invention to be performed by the perceptual preprocessor  110  based on such input signals. 
     Referring to FIG. 3, a flow chart of a perceptual preprocessing method  300  according to the invention is shown. In step  302 , the current frame F k  and the previously reconstructed frame {circumflex over (F)} k−1 are input. Then, in step  304 , δ-function test, also known as the “bush-hogging” thresholding process, is performed on every pixel x such that:                δ        (   x   )       =     {         1           if                            F   k          (   x   )       -         F   ^       k   -   1            (   x   )                &gt;     T        (           F   ^       k   -   1            (     N        (   x   )       )       ,       F   k          (   x   )         )                 0       otherwise                   (   6   )                                
     where N(x) is the neighborhood of x. The term “F k (x)−{circumflex over (F)} k−1  (x)” is known in the art as a difference frame. It is to be appreciated that the size of the neighborhood N(x) for x involves temporal masking and perhaps spatial masking. This is due to the consideration that sometimes two successive pixels on the same line in the previously reconstructed frame {circumflex over (F)} k−1 , say (i, j−1) and (i,j), where each pixel x in the frame is characterized by a (row, column) coordinate, both have intensity difference magnitudes, with respect to their counterparts in the current frame F k , that may be above a certain perceptual threshold. In such case, rather than consider an independent pixel in setting threshold T, the intensities of neighboring pixels may be considered since it is known that human visual perception may be more sensitive to intensity change in a given neighborhood rather than in any single pixel. As such, it is to be appreciated that the model to determine the threshold function T({circumflex over (F)} k−1  (N(x)), F k  (x)) is non-trivial. Accordingly, a preferred approach which is practical and effective is to use the pixel at the same location in the previously reconstructed frame to represent N(x) and use only this pixel as a reference to model the threshold as T ({circumflex over (F)} k−1  (N(x))). Thus, each pixel x is tested and, if the intensity difference between pixel x in the current frame and the previously reconstructed frame is greater than the threshold function, then δ(x) equals 1 for that pixel, otherwise, δ(x) equals 0. By way of further example, threshold T may be set to a single intensity value that accommodates human visual perception levels based on psycho physical experimentation. In an alternative embodiment, explained below, the threshold value T is a function of a quantization parameter associated with the bit rate of the encoding process such that quality-adaptive thresholding is realized. It is to be understood that, given the teachings of the invention, one of skill in the art will contemplate other methods of modeling the threshold function. 
     In any case, the δ-fiction tests result in an intensity difference value being obtained for each comparison of a pixel in F k (x) with a corresponding pixel in {circumflex over (F)} k−1  (x). If the intensity difference value is greater than the perceptual threshold T, then a value of one (1) is assigned to the comparison, else a value of zero (0) is assigned. It is to be appreciated that the δ-fiction tests may be performed on an entire frame or on one or more macroblocks at a time. 
     Next, in step  306 , for the ith macroblock B i  of the current frame, the results of the δ-function test on every pixel x ∈B i  are collected and a skip-information-bit Δ i  is assigned as follows:                Δ   i     =     {         0           if                     ∑     x                 ε                 Bi                       δ        (   x   )           &gt;   n             1         otherwise   .                     (   7   )                                
     Thus, the δ(x) results are summed for a given macroblock and, if the sum is greater than a decision value n, then the skip-information-bit Δ i  is set to zero (0) indicating that the macroblock is to be motion compensated by the remainder of the encoder, for example, as explained above. However, if the sum is less than or equal to the decision value n, then the skip-information-bit Δ i  is set to one (1) indicating that the macroblock is to be skipped, that is, not motion compensated. Advantageously, if a current frame macroblock under evaluation contains pixels that, after comparison to similarly situated pixels in a previously reconstructed frame macroblock, do not result in a sum greater than the decision value, then that macroblock is not subjected to motion estimation/compensation. This results in a segmentation of a frame into macroblocks that have changed a visually perceptible amount from the previous frame and macroblocks that have not changed a visually perceptible amount from the previous frame. As a result, the computational effort otherwise associated with motion estimation/compensation of that macroblock is saved or may be allocated to motion estimation/compensation of another macroblock or some other encoder processing function. It is to be appreciated that the decision value n for the number of pixels surviving from bush-hogging or thresholding (δ-function tests) may be set to a default value of zero in order to provide a perceptually distortion-free macroblock. However, we have observed that a video signal generated with a small, non-zero value n can be indistinguishable from a video signal generated with n equal to zero. It is to be appreciated that using a decision value greater than zero permits the encoder  100  to skip more macroblocks so that there is a greater realization of savings with respect to computational effort without sacrificing picture quality. Lastly, in step  308 , the skip-information-bit Δ i  is sent to the motion estimator  112  to inform the motion estimator whether or not each macroblock should be motion compensated. The skip-information-bit is also provided to the transformer  116 , which then informs the other affected components of the encoder  100  (i.e., quantizer  118 , entropy encoder  120 , etc.), that a particular macroblock is not to be processed thereby. That is, once a macroblock is identified as one to be skipped, the macroblock is not motion estimated/compensated and therefore the transformer  116 , the quantizer  118 , and the entropy encoder  120  do not process data associated with that particular macroblock. A corresponding decoder may simply repeat the corresponding macroblock from a previous frame when generating the corresponding decoded video image. 
     Referring now to FIG. 4, a flow chart of an alternative embodiment of a perceptual preprocessing method  400  according to the invention is shown. In step  402 , the current frame F k  and the previously reconstructed frame {circumflex over (F)} k−1  are input to the preprocessor  110 . In addition, the quantization parameter (QP) is provided to the preprocessor  110  by the rate control processor  130 . Then, in step  404 , a δ-function test is performed on every pixel x such that:                δ        (   x   )       =     {         1           if                            F   k          (   x   )       -         F   ^       k   -   1            (   x   )                &gt;   T             0       otherwise                   (   8   )                                
     where the visually perceptible threshold value T is modeled by a fixed value as follows: 
     
       
         T=T 0 +·QP.  (9) 
       
     
     T 0  is the just-noticeable-distortion (JND) value, QP is the quantization parameter, and c is a constant. The constant serves to scale the quantization parameter and thus the threshold T. As mentioned above, the quantization parameter (QP) used in a quantizer of an encoder generally has a substantial effect on the resultant bit rate: a large QP performs coarse quantization, reducing the bit rate and the resulting video quality, while a small QP performs finer quantization, which leads to a higher bit rate and higher resulting image quality. Given this relationship and the realization that the visual discrimination capability of the HVS is weaker in the presence of poorer quality video, the present invention provides for adjusting the visually perceptible threshold T as the QP of the encoder is adjusted by the rate control processor  130 . As a result, when the QP is increased by the rate control processor and the resulting video quality is generally poorer, the threshold T is increased according to the relationship in equation (9). Thus, the amount of intensity difference values that are above the threshold decreases generally resulting in fewer macroblocks being identified as requiring motion compensation. Conversely, when the QP is decreased by the rate control processor and the resulting video quality is generally higher, the threshold T is decreased according to the relationship in equation (9). Thus, the amount of intensity difference values that are above the threshold increases generally resulting in more macroblocks being identified as requiring motion compensation. Accordingly, by making the visually perceptible threshold T a function of QP, the present invention provides for video quality-adaptive thresholding, that is, as video quality decreases, an encoder implementing such an inventive technique motion compensates fewer macroblocks thus realizing increased computational savings. 
     Recall that in the illustrative embodiment depicted in FIG. 3, the number of surviving pixels are compared with the decision value n to determine whether a particular macroblock is to be encoded or not. However, the location of some of the surviving pixels can be random, that is, surviving pixels may be connected or isolated. Given this fact, we observe that the picture discrepancy due to the elimination of isolated pixels, referred to as “perceptual thinning,” is difficult for the HVS to detect. Thus, in order to take advantage of this, the present invention performs the following steps. In step  406 , the ith macroblock B i  of the current frame is divided into sub-blocks S i   j  such that ∪ j  S i   j =B i , that is, the union (∪ is the logic “OR” operator) of all the sub-blocks S i   j  equals the macroblock B i . For example, assuming a macroblock has 8×8 pixels, each macroblock may be divided into four 4×4 sub-blocks. Then, in step  408 , the results of the δ-function tests (step  404 ) are collected for the pixels in S i   j  and a variable Δ i   j  associated with each sub-block is assigned a value as follows:                Δ   j   i     =     {         0           if                     ∑     x                 ε                   S   j   i                         δ        (   x   )           &gt;     n   s               1       otherwise                   (   10   )                                
     where n s  is a sub-block decision value. In step  410 , the skip-information-bit Δ i  for the macroblock is determined as follows:                Δ   i     =       ⋂     x                 ε                 Bi            Δ   j   i               (   11   )                                
     where ∩ is the logic “AND” operator. Accordingly, if Δ i  is equal to zero (0) for a given macroblock, that macroblock is motion compensated. If Δ i  is equal to one (1) for a given macroblock, that macroblock is skipped. Lastly, in step  412 , a Δ i  signal is sent to the motion estimator  112  instructing it whether or not the macroblock is to be motion compensated. Also, the skip-information-bit is provided to the transformer  116 , which then informs the other affected components of the encoder  100  (i.e., quantizer  118 , entropy encoder  120 , etc.), that a particular macroblock is not to be processed thereby. That is, once a macroblock is identified as one to be skipped, the macroblock is not motion estimated/compensated and therefore the transformer  116 , the quantizer  118 , and the entropy encoder  120  do not process data associated with that particular macroblock. A corresponding decoder may simply repeat the corresponding macroblock from a previous frame when generating the corresponding decoded video image. 
     By way of example and comparison to the method of FIG. 3, assume that a macroblock under consideration by the preprocessor  110  includes 8×8 pixels and after the δ-function test step, it is determined that four pixels in the current frame result in intensity differences above the visually perceptible threshold. Assuming that the decision value n in the method of FIG. 3 is zero, the macroblock with the four pixel intensity differences above the threshold will be motion compensated. However, in accordance with the method of FIG. 4, by dividing the same macroblock into sub-blocks and collecting the results of the δ-function tests on each sub-block, it is known which sub-blocks contain which of the four pixels resulting in intensity differences above the threshold. Assume the 8×8 macroblock is divided into four 4×4 sub-blocks and assume there is one pixel in each of the four sub-blocks. If the sub-block decision value is two in step  408  of FIG. 4, then each sub-block will be assigned a value of one (1) such that Δ i  is equal to one (1) and the macroblock will be skipped. As a result, while the macroblock under consideration is motion compensated in the method of FIG. 3, it will not be motion compensated in the method of FIG. 4 due to the isolation of the location of each identified pixel. 
     Accordingly, the illustrative alternative embodiment of the invention enhances perceptual preprocessing by taking advantage of the inability of the HVS to distinguish the randomness of pixel locations in the difference frame in order to skip more macroblocks. Also, the illustrative alternative embodiment of the invention provides a quality-adaptive technique that capitalizes on the weaker discrimination capability of the HVS with respect to lower quality video in order to provide further computational savings. Further, it is to be appreciated that while the alternative embodiment above illustrates a perceptual preprocessing method that includes both a quality-adaptive thresholding process and a randomness-adaptive decision process, video encoders formed according to the invention may implement one or the other process and still realize significant computational savings. The same is true for any of the inventive techniques described above, that is, they may be implemented individually, or one or more in combination. 
     It should be understood that an encoder implementing these inventive compression techniques then need only transmit or store motion vectors for those macroblocks that are motion compensated, that is, those macroblocks that have been identified as having changed a visually perceptible degree from a corresponding macroblock of the previous frame. Advantageously, a corresponding video decoder need only inverse motion compensate these motion compensated macroblocks and repeat the corresponding macroblocks from the previous frame for the macroblocks of the current frame that were not motion compensated. Thus, improved computational efficiency is realized in a video decoder according to the coding techniques of the invention. 
     EXPERIMENTAL RESULTS 
     It is to be appreciated that the teachings of the present invention may be implemented in a wide variety of video applications and, as such, the examples discussed herein are not intended to limit the invention. By way of example, the encoding techniques of the invention may be employed to provide a low complexity video compression solution in order to realize a software-only low bit rate video coding system. Therefore, experiments were conducted on an H.263+ system using a TMN-8 codec as described in “Draft Text of Recommendation H.263 Version 2 (“H.263+”) for Decision,” ITU-T SG 16, December 1997. Three QCIF (176*144, 99 MBs/frame) sequences, respectively referred to as “claire,” “foreman,” and “mom-daughter,” were coded at 10 frames/second based on a full-search motion estimation scheme. FIGS. 5A through 5C show tabular simulation results including average bit rates, average PSNRs for the luminance component and the average number of skipped macroblocks to approximate the savings of computational complexity. “No Preprocessing” in the third columns of the tables means that the techniques of the perceptual preprocessor  110  were not applied. Non-perceptual preprocessing in the fourth columns uses the sum of absolute difference based on zero displaced motion vector (SAD 0 ) as the decision device: if SAD 0 &lt;T S , the macroblock will be skipped, where T S  is set equal to 400. The fifth columns results are attained from the preprocessing scheme of FIG. 3 when T=12 and n=3. The sixth columns results are attained from the preprocessing scheme including a quality-adaptive thresholding process when T 0 =10 and c=0.5. The last columns results are attained from the preprocessing scheme including both a quality-adaptive thresholding process and a randomness-adaptive decision process which divides a 16×16 macroblock into 16 4×4 sub-blocks when n s =2, T 0 =10 and c=0.5. Depending on the picture content, the techniques of the present invention accomplish different degrees of improvement in computational complexity without loss of perceived picture quality. 
     Based on the simulation results presented above, it is evident that the perceptual preprocessor of the invention reduces computational complexity without a perceived loss in picture quality. The computation overhead for the techniques of the invention is relatively small. This is critical to any application that has limited computing capability and restricted transmission bandwidth such as, for example, H.263 applications. However, the techniques of the invention are fully compatible with video standards such as, for example, all H.26x and MPEG families. 
     It should be noted that the elements of the encoder  100  may be implemented using a central processing unit, microprocessor, application-specific integrated circuit or other data processing device in a computer, video transmitter, cable system headend, television set-top box or other type of video signal processor. The central processing unit, microprocessor, application-specific integrated circuit or other data processing device may also have memory associated therewith for storing data and results associated with each elements function when necessary. The invention may be utilized in conjunction with numerous types of video processing or transmission systems, including but not limited to global computer networks such as the Internet, wide area networks, local area networks, cable systems, satellite systems and standard wired telephone networks. 
     Although illustrative embodiments of the present invention have been described herein with reference to the accompanying drawings, it is to be understood that the invention is not limited to those precise embodiments, and that various other changes and modifications may be affected therein by one skilled in the art without departing from the scope or spirit of the invention.