Image modeling with parametric texture sources for design and analysis of image processing algorithms

A novel statistical image model is proposed to facilitate the design and analysis of image processing algorithms. A mean-removed image neighborhood is modeled as a scaled segment of a hypothetical texture source, characterized as a 2-D stationary zero-mean unit-variance random field, specified by its autocorrelation function. Assuming that statistically similar image neighborhoods are derived from the same texture source, a clustering algorithm is developed to optimize both the texture sources and the cluster of neighborhoods associated with each texture source. Additionally, a novel parameterization of the texture source autocorrelation function and the corresponding power spectral density is incorporated into the clustering algorithm. The parametric auto-correlation function is anisotropic, suitable for describing directional features such as edges and lines in images. Experimental results demonstrate the application of the proposed model for designing linear predictors and analyzing the performance of wavelet-based image coding methods.