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Lossless image data sequence compression using optimal context quantization

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3 Author(s)
Forchhammer, S. ; Tech. Univ. Denmark, Lyngby, Denmark ; Xiaolin Wu ; Andersen, J.D.

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

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Data Compression Conference, 2001. Proceedings. DCC 2001.

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