Block-Matching Translation And Zoom Motion-Compensated Prediction by sub-sampling
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. Some attempts toward a more general motion model 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 in a practical way. It adopts the camera zooming and object motions that becomes zooming while projected on the video frames. The proposed BTZMP significantly improve motion compensated prediction. Experimental results show that BTZMP can give prediction gain up to 1.09dB compared to conventional sub-pixel block-matching MCP. In addition, BTZMP can be incorporated with Multiple Reference Frames (MRF) technique to give extra improvement, evidentially by the prediction gain ranging up to 2.08dB in the empirical simulations.