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Optimal Context Quantization in Lossless Compression of Image Data Sequences
2004
IEEE Transactions on Image Processing
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer that quantizes the -dimensional causal context ) of a source symbol into one of a set of
doi:10.1109/tip.2003.822613
pmid:15376585
fatcat:spslvcogtfcwvckzga5ikn35um