Analysis of motion-compensated temporal filtering versus motion-compensated prediction
Yongjun Wu, John W. Woods · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
In previous work, a performance bound for multi-hypothesis motion-compensated prediction (MCP) has been derived based on a video signal model with independent Gaussian displacement errors. A simplified form of the result is derived in this work. A performance bound for optimal motion-compensated temporal filtering (MCTF) has also been proposed based on a signal model with correlated Gaussian displacement errors. In this previous work, the optimal MCTF (KLT) was found to perform better than one-hypothesis MCP but not better than infinite-hypothesis MCP. In this work, we derive the performance of multi-hypothesis MCP again based on the signal model with correlated Gaussian displacement errors. Now with the same signal model, we find that optimal MCTF has the same performance as that of infinite-hypothesis MCP.