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