Lossless image compression by adaptive contextual encoding

Bruno Aiazzi; Luciano Alparone; Stefano Baronti

doi:10.1117/12.383001

19 April 2000 Lossless image compression by adaptive contextual encoding

Bruno Aiazzi, Luciano Alparone, Stefano Baronti

Proceedings Volume 3974, Image and Video Communications and Processing 2000; (2000) https://doi.org/10.1117/12.383001
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

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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