Motion compensation based on implicit block segmentation

Abstract

Block-based motion and disparity compensation are popular techniques to exploit correlation between video frames. Block sizes used for compensation can be chosen to achieve a good trade-off between signaling overhead and prediction accuracy. However, motion field boundaries correspond to objects having arbitrary shapes; this limits the accuracy of block-based compensation, even when small block sizes are chosen. In this paper we seek to enable compensation based on arbitrarily-shaped regions, while preserving an essentially block-based compensation architecture. To do so, we propose tools for implicit block-segmentation and predictor selection. Given two candidate block predictors, segmentation is applied to the difference of predictors. Then a weighted sum of predictors in each segment is selected for prediction. Simulation results show improvements in rate-distortion (R-D) performance, as compared to the standard quad tree approach in H.264/AVC.