A commonality modeling framework for enhanced video coding leveraging on the cuboidal partitioning based representation of frames

Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. Modern video coding systems are block-based wherein commonality modeling is carried out only from the perspective of the block that need be coded next. In this work, we argue for a commonality modeling approach that can provide a seamless blending between global and local homogeneity information. For this purpose, at first the frame that need be coded, is recursively partitioned into rectangular regions based on the homogeneity information of the entire frame. After that each obtained rectangular region's feature descriptor is taken to be the average value of all the pixels; intensities encompassing the region. In this way, the proposed approach generates a coarse representation of the current frame by minimizing both global and local commonality. This coarse frame is computationally simple and has a compact representation. It attempts to preserve important structural properties of the current frame which can be viewed subjectively as well as from improved rate-distortion performance of a reference scalable HEVC coder that employs the coarse frame as a reference frame for encoding the current frame.