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Proceedings Paper

Trends in lossless image compression: adaptive vs. classified prediction and context modeling for entropy coding
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Paper Abstract

This paper discusses the most recent trends in the reversible intraframe compression of grayscale images. With reference to a spatial DPCM scheme, prediction, either linar or nonlinear, 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 optimum MMSE predictors are calculated. A trade- off between the above two strategies is proposed. It relies on a classified linear-regression prediction obtained through fuzzy techniques, followed by context-based modeling of the outcome prediction errors, to enhance entropy coding. The present scheme is a reworking of a fuzzy encode previously presented by the authors. Now, predictors, instead of pixel intensity patterns, are fuzzy-clustered to find out optimized MMSE prediction classes, and a novel membership function measuring the fitness of prediction is adopted. A thorough performances comparison with the most advanced methods in the literature highlights advantages, and drawbacks as well, of the fuzzy approach.

Paper Details

Date Published: 16 December 1999
PDF: 11 pages
Proc. SPIE 3814, Mathematics of Data/Image Coding, Compression, and Encryption II, (16 December 1999); doi: 10.1117/12.372744
Show Author Affiliations
Bruno Aiazzi, Research Institute on Electromagnetic Waves (Italy)
Luciano Alparone, Univ. of Florence (Italy)
Stefano Baronti, Research Institute on Electromagnetic Waves (Italy)


Published in SPIE Proceedings Vol. 3814:
Mathematics of Data/Image Coding, Compression, and Encryption II
Mark S. Schmalz, Editor(s)

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