Vector quantization of contextual information for lossless image compression

The authors present a new pruned tree structured vector quantization (TSVQ) algorithm, called incremental tree growing (ITG), which incrementally grows and quantizes the context tree locally on a level by level basis, thus drastically reducing both the memory requirements and the computational complexity. After the incremental tree growing is completed, terminal branches are globally vector quantized again, which is possible due to significant reduction of the number of initial branches. Using a technique similar in spirit to the mean removed VQ (MRVQ) of Baker and Gray (1983), significant reduction in the number of probability tables is achieved. In one of their simulations, they reduced the number of probability tables from 262 K(=2 ¹⁸) to 108 by using the ITG algorithm, and from 108 to 10 using the modified MRVQ at the conditional entropy increase of only 1.26%. In summary, the proposed ITG algorithm combined with a modified MRVQ provides an efficient framework for the design of context-trees with reduced memory and computational requirements