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19 April 2000 Lossless image compression by adaptive contextual encoding
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This paper deals with 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 (MMSE) predictors are calculated. In this paper, a trade off between the above two strategies is proposed, which relies on a classified linear-regression prediction obtained through fuzzy techniques, and is followed by context based statistical modeling of the outcome prediction errors, to enhance entropy coding. A thorough performances comparison with the most advanced methods in the literature highlights the advantages of the fuzzy approach.
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Bruno Aiazzi, Luciano Alparone, and Stefano Baronti "Lossless image compression by adaptive contextual encoding", Proc. SPIE 3974, Image and Video Communications and Processing 2000, (19 April 2000);

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