Inferring BP Priority Order Using 5D Tensor Voting for Inpainting-Based Macroblock Prediction

摘要

In this paper, we propose an optimized in painting-based macro block(MB) prediction mode (IP-mode) in the state-of-the-art H.264/AVC video compression engine, and belief propagation (BP) is applied to achieve the global spatio-temporal consistency between the predicted content and the co-located known region. To decrease the computing complexity of the iterative BP algorithm, we explore structure and motion features by tensor votes projected from the decoded regions, to assign the priority of message scheduling and prune the intolerable labels. No side information is need to be coded into the bit stream, while the structure and motion information is estimated from the decoded region at decoder side. Compared with the existing prediction modes in H.264/AVC, the proposed IP-mode only encode the macro block header and residual data, where the residual is lighter in homogeneous texture regions by the optimized BP algorithm with label pruning. Experiments validate that the proposed video compression scheme can achieve a better R-D performance, and the computing complexity is largely reduced through the inference of structure and motion features.