Euclidean Distance-Based Weighted Prediction for Merge Mode in HEVC

Abstract

Merge mode can achieve a considerable coding gain because of reducing the cost of coding motion information in video codecs. However, the simple adoption of the motion information from the neighbouring blocks may not achieve the optimal performance as the motion correlation between the pixels and the neighbouring block decreases with their distance increasing. To address this problem, the paper proposes a Euclidean distance-based weighted prediction algorithm as an additional candidate in the merge mode. First, several predicted blocks are generated by motion compensation prediction (MCP) with the motion information from available neighbouring blocks. Second, an additional predicted block is generated by a weighted average of the predicted blocks above, where the weighted coefficient is related to Euclidean distances from the neighbouring candidate to the pixel points in the current block. Finally, the best merge mode is selected by the rate distortion optimization (RDO) among the original merge candidates and the additional candidate. Experimental results show that, on the joint exploration test model 7.0 (JEM 7.0), the proposed algorithm achieves better coding performance than the original merge mode under all configurations including random access (RA), low delay B (LDB), and low delay P (LDP), with a slight coding complexity increase. Especially for the LDP configuration, the proposed method achieves 1.50% bitrate saving on average.