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Appendix E
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code word assignment, the actual bit rate compression is achieved. The quantization process is the source of virtually all the loss of information in the compression algorithm. This is an important point, as it simplifies the design process and facilitates fine-tuning of the system. The degree of subjective picture degradation caused by coefficient quantization tends to depend on the nature of the scenery being coded. Within a given picture, distortions of some regions may be less apparent than in others. The video coding system allows for the level of quantization to be adjusted for each macroblock in order to save bits, where possible, through coarse quantization. Perceptual Weighting. The human visual system is not uniformly sensitive to coefficient quantization error [10]. Perceptual weighting of each source of coefficient quantization error is used to increase quantization coarseness, thereby lowering the bit rate. The amount of visible distortion resulting from quantization error for a given coefficient depends on the coefficient number, or frequency, the local brightness in the original image, and the duration of the temporal characteristic of the error. The dc coefficient error results in mean value distortion for the corresponding block of pixels, which can expose block boundaries. This is more visible than higher-frequency coefficient error, which appears as noise or texture. Displays and the HVS exhibit nonuniform sensitivity to detail as a function of local average brightness. Loss of detail in dark areas of the picture is not as visible as it is in the brighter areas. Another opportunity for bit savings is presented in textured areas of the picture, where highfrequency coefficient error is much less visible than in relatively flat areas. Brightness and texture weighting require analysis of the original image because these areas may be well predicted. Additionally, limiting its duration to one or two frames can easily mask distortion. This effect is most profitably used after scene changes, where the first frame or two can be greatly distorted at normal speed without perceptible artifacts. When transform coefficients are being quantized, the differing levels of perceptual importance of the various coefficients can be exploited by allocating the bits to shape the quantization noise into the perceptually less important areas. This can be accomplished by varying the relative step sizes of the quantizers for the different coefficients. The perceptually important coefficients maybe quantized with a finer step size than the others. For example, low spatial frequency coefficients may be quantized finely and the less important high frequency coefficients may be quantized more coarsely. A simple method to achieve different step
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sizes is to normalize or weight each coefficient based on its visual importance. All of the normalized coefficients may then be quantized in the same manner, such as rounding to the nearest integer (uniform quantization). Normalization or weighting effectively scales the quantizer from one coefficient to another. The MPEG-2 video compression system utilizes perceptual weighting, wherein the different DCT coefficients are weighted according to a perceptual criterion prior to uniform quantization. Quantizer matrices determine the perceptual weighting. The compression system allows for modifying the quantizer matrices before each picture.
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Quantization creates an efficient, discrete representation for the data to be transmitted [10]. Code word assignment takes the quantized values and produces a digital bit stream for transmission. Hypothetically, the quantized values could be simply represented using uniform- or fixedlength code words. Under this approach, every quantized value would be represented with the same number of bits. As outlined previously in general terms, greater efficiency, in terms of bit rate, can be achieved with entropy coding. Entropy coding attempts to exploit the statistical properties of the signal to be encoded. A signal, whether it is a pixel value or a transform coefficient, has a certain amount of information, or entropy, based on the probability of the different possible values or events occurring. For example, an event that occurs infrequently conveys much more new information than one that occurs often. The fact that some events occur more frequently than others can be used to reduce the average bit rate. Huffman Coding. Huffman coding, which is utilized in the ATSC DTV video compression system, for example, is one of the most common entropy coding schemes [10]. In Huffman coding, a codebook is generated that can approach the minimum average description length (in bits) of events, given the probability distribution of all the events. Events that are more likely to occur are assigned shorter-length code words and those less likely to occur are assigned longer-length code words. Run Length Coding. In video compression, most of the transform coefficients frequently are quantized to zero [10]. There may be a few non-zero low-frequency coefficients and a sparse scattering of non-zero
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