Semi-adaptive context-tree based lossless image compression

High order statistical modeling is a promising technique for data compression. Previously we introduced an algorithm for the design of efficient high order statistical models of digital images. The system consisted in mapping the context set to a tree-structured vector quantizer, the context-tree, and using vector quantization techniques to reduce the size of the tree and, thus, simplify the model. While the models obtained through this off-line design procedure are very efficient they can only be used to code images in the same class as those represented in the training set. In this paper, we show that high order models can be obtained adaptively by constructing the context-tree with a wider range of image classes and allowing the coder to adaptively collect the statistics as the input is processed. Since the structure of the model (context-tree) is kept fixed and only the conditional distributions are allowed to vary the overall scheme is semi-adaptive. The compression efficiency is better than the conventional DPCM+entropy scheme by 8% to 36% for the images used for the simulations