BLOCK-MATCHING TRANSLATION AND ZOOM MOTION-COMPENSATED PREDICTION

In modern video coding standards, motion compensated prediction (MCP) plays a key role to achieve video compression efficiency. Most of them make use of block matching techniques and assume the motions are pure translational. Attempts toward a more general motion model are usually too complex to be practical in near future. In this paper, a new Block-Matching Translation and Zoom Motion-Compensated Prediction (BTZMP) is proposed to extend the pure translational model to a more general model with zooming. It adopts the camera zooming and object motions that becomes zooming while projected on video frames. Experimental results show that BTZMP can give prediction gain up to 2.25dB for various sequences compared to conventional block-matching MCP. BTZMP can also be incorporated with multiple reference frames technique to give extra improvement, evidentially by the prediction gain ranging from 2.03 to 3.68dB in the empirical simulations.