Lossless image data sequence compression using optimal context quantization

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Context based entropy coding often faces the conflict of a desire for large templates and the problem of context dilution. We consider the problem of finding the quantizer Q that quantizes the K-dimensional causal context Ci=(X(i-t1), X(i-t2), …, X(i-tK)) of a source symbol Xi into one of M conditioning states. A solution giving the minimum adaptive code length for a given data set is presented (when the cost of the context quantizer is neglected). The resulting context quantizers can be used for sequential coding of the sequence X0, X1, X 2, …. A coding scheme based on binary decomposition and context quantization for coding the binary decisions is presented and applied to digital maps and α-plane sequences. The optimal context quantization is also used to evaluate existing heuristic context quantizations.
Original languageEnglish
JournalData Compression Conference. Proceedings
Pages (from-to)53-62
Publication statusPublished - 2001
Event2001 IEEE Data Compression Conference - Snowbird, UT, United States
Duration: 27 Mar 200129 Mar 2001


Conference2001 IEEE Data Compression Conference
CountryUnited States
CitySnowbird, UT
Internet address

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