Texture Representations for Image and Video Synthesis

Abstract

In texture synthesis and classification, algorithms require a small texture to be provided as an input, which is assumed to be representative of a larger region to be re-synthesized or categorized. We focus on how to characterize such textures and automatically retrieve them. Most works generate these small input textures manually by cropping, which does not ensure maximal compression, nor that the selection is the best representative of the original. We construct a new representation that compactly summarizes a texture, while using less storage, that can be used for texture compression and synthesis. We also demonstrate how the representation can be integrated in our proposed video texture synthesis algorithm to generate novel instances of textures and video hole-filling. Finally, we propose a novel criterion that measures structural and statistical dissimilarity between textures.