Automatic neighborhood selection for SAR models applied to gray texture

Abstract

Textures can be broadly divided into two categories, namely, stochastic and deterministic. The stochastic textures are characterized by its statistical properties and do not have easily identifiable primitives. Even if one can extract such primitives, a placement rule description for such textures may be extremely complicated. One of the ways to describe and generate such textures is Simultaneous Autoregressive (SAR) Linear Prediction models. The major difficulty in utilizing this model is choosing proper neighborhood locations, within which pixels are considered interdependent. The work presented here emphasizes the automatic neighbor location selection based on sample correlation function. The algorithm is stretched to the possible extent with rigorous experimentation that proves the decorrelation phenomenon with residual image. Two new methods viz. 'actual residual image' and 'uniform noise transformed to a noise with histogram matched to residual image' are suggested to synthesize texture towards perceptual quality improvement.