Lossless image data sequence compression using optimal context quantization

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.