Optimal Context Quantization in Lossless Compression of Image Data Sequences

S. Forchhammer, X. Wu, J.D. Andersen
2004 IEEE Transactions on Image Processing  
In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer that quantizes the -dimensional causal context ) of a source symbol into one of a set of
more » ... itioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of -ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and -plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.
doi:10.1109/tip.2003.822613 pmid:15376585 fatcat:spslvcogtfcwvckzga5ikn35um