Manhattan distance based inter-frame weighted prediction algorithm

Merge mode saves the number of bits required to encode motion information by sharing the motion vector (MV) in neighboring blocks, which improves the rate-distortion performance of encoders effectively. However, motion compensation prediction (MCP) is not accurate enough in the merge mode currently. Therefore, this paper analyses the characteristics of residual distribution after MCP in the merge mode, and presents a Manhattan distance based weighted prediction method as an additional candidate for the merge mode. First, several predicted blocks are obtained by MCP with motion vectors in neighboring candidates. Second, the additional candidate is obtained by a weighted average method according to Manhattan distances from the neighboring candidate to the pixel points in the predicted blocks obtained. Finally, the best merge mode is selected by rate distortion optimization (RDO) among the additional candidate and the original candidates. The experimental results show that, on the joint exploration test model 7.0 (JEM 7.0), the proposed method achieves rate distortion performance improvement under the different configurations of encoder, where a bitrate saving of 1.34% on average is obtained under the configuration of low delay P frame.