Region-adaptive transform based on a stochastic model

This paper is concerned with linear transforms for arbitrarily-shaped image segments. In contrast to other techniques described in the literature, the proposed transform is based upon a stochastic model of image covariance within the considered region. Emerging from a separable stationary Markov model proposed for rectangular regions, we derive a non-stationary Markov model with natural boundary conditions. We compute its eigentransform, which is the optimum linear transform under a broad variety of performance measures. For the special case of a rectangular region, the method yields the DCT basis functions. Simulation results for natural imagery are provided