Joint scalable modeling of motion and boundary geometry with quad-tree node merging

Abstract

Quad-tree structures are often used to model motion between frames of a video sequence. In this study we are interested in the bit-rate efficiency and resolution scalability of quad-tree motion models. Recent publications have reported improvements to motion modeling efficiency achieved by introducing geometry information to nodes of quad-tree structures. The benefits of leaf merging to tree based representations have also been recently highlighted. In keeping with these recent findings we explore scalability in the context of merged quad-tree representations, modeling the underlying motion flow with joint geometry and motion models. We employ hierarchical coding of the merged quad-tree and ensure that the merging process retains the property of resolution scalability. We show that the performance of scalable coding can be significantly improved by incorporating a new cost objective which takes into account the possibility of scalable decoding. Experimental results reveal that these scalability improvements are achieved without significant loss in overall efficiency and with competitive performance at all resolutions.