Applications of universal context modeling to lossless compression of gray-scale images

Abstract

Inspired by theoretical results on universal modeling, a general framework for sequential modeling of gray-scale images is proposed and applied to lossless compression. The model is based on stochastic complexity considerations and is implemented with a tree structure. It is efficiently estimated by a modification of the universal algorithm context. The sequential, lossless compression schemes obtained when the context modeler is used with an arithmetic coder, are tested with a representative set of gray-scale images. The compression ratios are compared with those obtained with state-of-the-art algorithms available in the literature, with the results of the comparison, showing the potential of the proposed approach.