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-t₁), X(i-t₂), …, X(i-t_K)) of a source symbol X_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 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