Nonlinear prediction in the 2D Wold decomposition for texture modeling

The problem of image texture stochastic modeling by means of the 2D Wold decomposition is addressed. Specifically, the 2D Wold decomposition additively separates a texture into a deterministic and an indeterministic component. Since these components are shown to be statistically orthogonal, for textures having a distribution that significantly deviates from Gaussianity, it is possible, in principle, to establish some link between the two components. The aim of this contribution is to show how the indeterministic component can be predicted from the deterministic one by means of suitable nonlinear schemes. The hierarchical structure of the 2D Wold decomposition is thus generalized to deal with the non-Gaussian behavior of most of the natural textures of interest.