Asymptotically Admissible Texture Synthesis
Recently there is a resurgent interest in example based texture analysis and synthesis in both computer vision and computer graphics. While study in computer vision is concerned with learning accurate texture models, research in graphics is aimed at effective algorithms for texture synthesis without necessarily obtaining explicit texture model. This paper makes three contributions to this recent excitement. First, we introduce a theoretical framework for designing and analyzing texture sampling algorithms. This framework, built upon the mathematical definition of textures, measures a texture sampling algorithm using admissibility, effectiveness, and sampling speed. Second, we compare and analyze texture sampling algorithms based on admissibility and effectiveness. In particular, we propose different design criteria for texture analysis algorithms in computer vision and texture synthesis algorithms in computer graphics. Finally, we develop a novel texture synthesis algorithm which samples from a subset of the Julesz ensemble by pasting texture patches from the sample texture. A key feature of our algorithm is that it can synthesize high-quality textures extremely fast. On a mid-level PC we can synthesize a 512 512 texture from a 64 64 sample in just 0.03 second. This algorithm has been tested through extensive experiments and we report sample results from our experiments.