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 prediction 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.
Fuzzy blending of relaxation labeled predictors for high-performance lossless image compression / Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano. - STAMPA. - 3962:(2000), pp. 41-49. (Intervento presentato al convegno Applications of Artificial Neural Networks in Image Processing V tenutosi a San Jose, CA, USA nel 27-28 January 2000) [10.1117/12.382921].
Fuzzy blending of relaxation labeled predictors for high-performance lossless image compression
ALPARONE, LUCIANO;
2000
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 prediction 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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.