Texture synthesis using gray-level co-occurrence models: algorithms, experimental analysis, and psychophysical support

Abstract

The development and evaluation of texture synthesis algo- rithms is discussed. We present texture synthesis algorithms based on the gray-level co-occurrence (GLC) model of a texture field. These algo- rithms use a texture similarity metric, which is shown to have high cor- relation with human perception of textures. Synthesis algorithms are evaluated using extensive experimental analysis. These experiments are designed to compare various iterative algorithms for synthesizing a ran- dom texture possessing a given set of second-order probabilities as characterized by a GLC model. Three texture test cases are selected to serve as the targets for the synthesis process in the experiments. The three texture test cases are selected so as to represent three different types of primitive texture: disordered, weakly ordered, and strongly or- dered. For each experiment, we judge the relative quality of the algo- rithms by two criteria. First, we consider the quality of the final synthe- sized result in terms of the visual similarity to the target texture as well as a numerical measure of the error between the GLC models of the syn- thesized texture and the target texture. Second, we consider the relative computational efficiency of an algorithm, in terms of how quickly the algorithm converges to the final result. We conclude that a multiresolu- tion version of the ''spin flip'' algorithm, where an individual pixel's gray level is changed to the gray level that most reduces the weighted error between the images second order probabilities and the target probabili- ties, performs the best for all of the texture test cases considered. Finally, with the help of psychophysical experiments, we demonstrate that the results for the texture synthesis algorithms have high correlation with the texture similarities perceived by human observers. © 2001 Society of Photo-