A Parametric Framework for Video Compression Using Region-Based Texture Models

This paper presents a novel means of video compression based on texture warping and synthesis. Instead of encoding whole images or prediction residuals after translational motion estimation, our algorithm employs a perspective motion model to warp static textures and utilizes texture synthesis to create dynamic textures. Texture regions are segmented using features derived from the complex wavelet transform and further classified according to their spatial and temporal characteristics. Moreover, a compatible artifact-based video metric (AVM) is proposed with which to evaluate the quality of the reconstructed video. This is also employed in-loop to prevent warping and synthesis artifacts. The proposed algorithm has been integrated into an H.264 video coding framework. The results show significant bitrate savings, of up to 60% compared with H.264 at the same objective quality (based on AVM) and subjective scores.