Lossless compression of multi/hyper-spectral imagery based on a 3-D fuzzy prediction

This paper describes an original application of fuzzy logic to the reversible compression of multispectral data. The method consists of a space-spectral 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 a set of linear regression predictors. Pixels both on the current band and on previously encoded bands may be used to define a causal neighborhood. The coefficients of each predictor are calculated so as to minimize the mean-squared error for those pixels whose intensity level patterns lying on the causal neighborhood, belong in a fuzzy sense to a predefined cluster. The size and shape of the causal neighborhood, as well as the number of predictors to be switched, may be chosen by the user and determine the tradeoff between coding performances and computational cost. The method exhibits impressive results, thanks to the skill of predictors in fitting multispectral data patterns, regardless of differences in sensor responses.