Picture-level parameteric motion representation for efficient motion compensation

When a picture contains non-translational motions in it, a picture-level parametric motion model can be more efficient than the block-based translational motion model because the former has small number of parameters that can replace many motion vectors of individual blocks. In addition, the former can represent the deformation of the image better than the latter. Based on this idea, we detected multiple homography transformations between a reference and a current picture. Then, we generated warped reference pictures¹ corresponding to the homographies and inserted the one of the warped reference pictures into reference picture lists. We measured the performance of the proposed algorithm using the test condition that is proposed by MPEG and ITU recently. Experimental result showed 3.1% overall bitrate saving under low delay & high efficiency condition and 3.5% overall bitrate saving under random access & high efficiency condition compared with TMuC 0.7.3.