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19 April 2000 Lossless image compression by adaptive contextual encoding
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Abstract
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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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); https://doi.org/10.1117/12.383001
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