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 $C_{i}$ = $(X_{i-{\rm t}_{1}}, X_{i-t_{2}}, \ldots, X_{i-{\rm t}_{K}})$ of a source symbol $X_{l}$ into one of $M$ conditioning states. A solution giving the minimum adaptive code length for a given data set is presented …