Models for Static and Dynamic Texture Synthesis in Image and Video Compression

In this paper, we investigate the use of linear, parametric models of static and dynamic texture in the context of conventional transform coding of images and video. We propose a hybrid approach incorporating both conventional transform coding and texture-specific methods for improvement of coding efficiency. Regarding static (i.e., purely spatial) texture, we show that Gaussian Markov random fields (GMRFs) can be used for analysis/synthesis of a certain class of texture. The properties of this model allow us to derive optimal methods for classification, analysis, quantization and synthesis. For video containing dynamic textures, a linear dynamic model can be derived from frames encoded in a conventional fashion. We show that after removing effects from camera motion, this model can be used to synthesize further frames. Beyond that, we show that using synthesized frames in an appropriate fashion for prediction leads to significant bitrate savings while preserving the same peak signal-to-noise ratio (PSNR) for sequences containing dynamic textures.