Lossless image compression based on an enhanced fuzzy regression prediction

An effective method for lossless image compression is presented. 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 the fuzzy encoder presented at ICIP'98 (FDC). 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. Size and shape of causal neighborhoods supporting prediction, as well as number of predictors to be blended, may be chosen by user and settle the tradeoff between coding performances and computational costs. The encoder exhibits impressive performances, thanks to the skill of predictors in fitting data patterns as well as to context modeling.