Optimal Linear Processing for Image and Video Coding
نویسندگان
چکیده
Linear methods form the basis of current high-performance image and video coders. In image coding, energy-compacting linear transforms are utilized to decorrelate and sparsify the source. In the coding of video sequences, a simple linear predictive loop is used to reduce the important temporal dependencies. This dissertation presents a body of work concerned with the analysis and optimization of the linear processing employed in the compression of still images and video sequences. The transform coding of images is analyzed from a common standpoint in order to generate a framework for the design of optimal transforms. Using a general energy-com-paction measure, the relationship between optimal transforms and optimal linear estim-ators is determined, resulting in energy-compaction-optimized nonorthogonal transforms. On the class of signals exhibiting localized discontinuities (edges), it is shown that the global statistics of the class are corrupted by the presence of edges and do not reeect the possibly strong local dependencies. Using a statistical model, we show that coding-eecient transforms for this class must be localized. In particular, we establish the rate-distortion performance of wavelet representations on this class. The diierential pulse-coded modulation (DPCM) loop employed in the coding of video sequences is ineecient at low bit rates. Using a Gaussian auto-regressive model, we propose the incorporation of rate-distortion optimized lters into the DPCM loop. It is shown that these lters trade oo less important spectral components of the source for signiicant improvements in the rate-distortion performance of DPCM. iii ACKNOWLEDGMENTS
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تاریخ انتشار 1997