Affine Prediction as a Post Processing Stage
Roman C. Kordasiewicz, Michael Gallant, Shahram Shirani · 2007
Translational motion vectors (MV)s and macro block (MB) frame partitioning are the predominant means of motion estimation (ME) and motion compensation (MC). However, the translational motion model does not describe sufficiently complex motion such as rotation, zoom or shearing. To remedy this one can start computing more advanced motion parameters and/or partition the frame differently. However these approaches are either very computationally expensive and/or have limited search ranges. Thus, in this paper we propose a novel post processing stage which can be easily incorporated into most of the current coders. This stage generates the predictor for each inter MB, based on an affine motion model using translational motion vectors. Our approach has very low computational complexity, however average PSNR gains of up to 0.6 dB were realized for video sequences with complex motion.