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 C/sub i/=(X(i-t/sub 1/), X(i-t/sub 2/), ..., X(i-t/sub K/)) of a source symbol X/sub i/ 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 X/sub 0/, X/sub 1/, X/sub 2/, .... A coding scheme based on binary decomposition and context quantization for coding the binary decisions is presented and applied to digital maps and /spl alpha/-plane sequences. The optimal context quantization is also used to evaluate existing heuristic context quantizations.