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Predictive coding in the MPEG-2 compression algorithm exploits the temporal correlation in the sequence of image frames [10]. Motion compensation is a refinement of that temporal prediction, which allows the coder to account for apparent motions in the image that can be estimated. Aside from temporal prediction, another source of correlation that represents redundancy in the image data is the spatial correlation within an image frame or field. This spatial correlation of images, including parts of images that contain apparent motion, can be accounted for by a spatial transform of the prediction differences. In the intraframe-coding case, where there is by definition no attempt at prediction, the spatial transform applies to the actual picture data. The effect of the spatial transform is to concentrate a large fraction of the signal energy in a few transform coefficients. To exploit spatial correlation in intraframe and predicted portions of the image, the image-prediction residual pixels are represented by their DCT coefficients. For typical images, a large fraction of the energy is concentrated in a few of these coefficients. This makes it possible to code only a few coefficients without seriously affecting picture quality. The DCT is used because it has good energy-compaction properties and results in real coefficients. Furthermore, numerous fast computational algorithms exist for the implementation of DCT.

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Theoretically, a large DCT will outperform a small DCT in terms of coefficient decorrelation and block energy compaction [10] Better overall performance can be achieved, however, by subdividing the frame into many smaller regions, each of which is individually processed If the DCT of the entire frame is computed, the whole frame is treated equally For a typical image, some regions contain a large amount of detail and other regions contain very little Exploiting the changing characteristics of different images and different portions of the same image can result in significant improvements in performance To take advantage of the varying characteristics of the frame over its spatial extent, the frame is partitioned into blocks of 8 8 pixels The blocks then are independently transformed and adaptively processed based on their local characteristics.

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Partitioning the frame into small blocks before taking the transform not only allows spatially adaptive processing, but also reduces the computational and memory requirements The partitioning of the signal into small blocks before computing the DCT is referred to as the block DCT An additional advantage of using the DCT domain is that the DCT coefficients contain information about the spatial frequency content of the block By utilizing the spatial frequency characteristics of the human visual system, the precision with which the DCT coefficients are transmitted can be in accordance with their perceptual importance This is achieved through the quantization of these coefficients, as explained in the following sections Adaptive Field/Frame DCT As noted previously, the DCT makes it possible to take advantage of the typically high degree of spatial correlation in natural scenes [10].

When interlaced pictures are coded on a frame basis, however, it is possible that significant amounts of motion result in relatively low spatial correlation in some regions Allowing the DCTs to be computed either on a field basis or on a frame basis accommodates this situation The decision to use field- or frame-based DCT is made individually for each macroblock Adaptive Quantization The goal of video compression is to maximize the video quality at a given bit rate and this requires a careful distribution of the limited number of available bits [10] By exploiting the perceptual irrelevancy and statistical redundancy within the DCT domain representation, an appropriate bit allocation can yield significant improvements in performance Quantization is performed to reduce the precision of the DCT coefficient values Through quantization and.

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