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Proceedings Paper

Fuzzy clustering and soft switching of linear regression models for reversible image compression
Author(s): Bruno Aiazzi; Pasquale S. Alba; Luciano Alparone; Stefano Baronti
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Paper Abstract

This paper describes an original application of fuzzy logic to reversible compression of 2D and 3D data. The compression method consists of a space-variant prediction followed by context- based classification ad arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of a set of linear regression predictors. The coefficients of each predictor are calculated so as to minimize prediction MSE for those pixels whose graylevel patterns, lying on a causal neighborhood of prefixed shape, are vectors belonging in a fuzzy sense to one cluster. In the 3D case, pixels both on the current slice and on previously encoded slices may be used. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen before running the algorithm and determine the trade-off between coding performance sand computational cost. The method exhibits impressive performances, for both 2D and 3D data, mainly thanks to the optimality of predictors, due to their skill in fitting data patterns.

Paper Details

Date Published: 13 October 1998
PDF: 12 pages
Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); doi: 10.1117/12.326716
Show Author Affiliations
Bruno Aiazzi, Istituto di Ricerca sulle Onde Elettromagnetiche/CNR (Italy)
Pasquale S. Alba, Univ. of Florence (Italy)
Luciano Alparone, Univ. of Florence (Italy)
Stefano Baronti, Istituto di Ricerca sulle Onde Elettromagnetiche/CNR (Italy)

Published in SPIE Proceedings Vol. 3455:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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