Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression

Bruno Aiazzi; Luciano Alparone; Stefano Baronti

doi:10.1117/12.382921

14 April 2000 Fuzzy blending of relaxation-labeled predictors for high-performance lossless image compression

Bruno Aiazzi, Luciano Alparone, Stefano Baronti

Proceedings Volume 3962, Applications of Artificial Neural Networks in Image Processing V; (2000) https://doi.org/10.1117/12.382921
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.382921

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