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14 April 2000 Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression
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This paper deals with application of fuzzy and neural techniques to the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction 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 minimum MSE predictors are calculated. Here, a trade off between the above two strategies is proposed, which relies on a space-varying linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of predictive errors, to enhance entropy coding. A thorough comparison with the most advanced methods in the literature, as well as an investigation of performance trends to work parameters, highlight the advantages of the fuzzy approach.
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Bruno Aiazzi, Luciano Alparone, and Stefano Baronti "Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000);

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