Fuzzy clustering and soft switching of linear regression models for reversible image compression

This paper describes an original application of fuzzy logic to reversible compression of 2D and 3D data. The compression method consists of a space-varying prediction followed by context-based classification and arithmetic coding of the outcome residuals. Prediction of a pixel to be encoded is obtained from the fuzzy-switching of ct 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 an 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 performances and 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.