Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding

This paper discusses the most recent trends in the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction, either linar or nonlinear, may be accomplished in a space-varying fashion following two main strategies: adaptive, i.e., with predictors recalculated at each pixel position, and classified, in which image blocks, or pixels are preliminarily labeled into a number of statistical classes, for which optimum MMSE predictors are calculated. A trade-off between the above two strategies is proposed. It relies on a classified linear-regression prediction obtained through fuzzy techniques, followed by context-based modeling of the outcome prediction errors, to enhance entropy coding. The present scheme is a reworking of a fuzzy encoder previously presented by the authors. Now, predictors, instead of pixel intensity patterns, are fuzzy-clustered to find out optimized MMSE prediction classes, and a novel membership function measuring the fitness of prediction is adopted. A thorough performances comparison with the most advanced methods in the literature highlights advantages, and drawbacks as well, of the fuzzy approach.