Optimizing Motion Compensated Prediction for Error Resilient Video Coding

Abstract

Abstract-This paper is concerned with optimization of the motion compensated prediction framework to improve the error resilience of video coding for transmission over lossy networks. First, accurate end-to-end distortion estimation is employed to optimize both motion estimation and prediction within an overall rate-distortion framework. Low complexity practical variants are proposed: a method to approximate the optimal motion via simple distortion and source coding rate models, and a source-channel prediction method that uses the expected decoder reference frame for prediction. Second, reference frame generation is revisited as a problem of filter design to optimize the error resilience versus coding efficiency tradeoff. The special cases of leaky prediction and weighted prediction (i.e., finite impulse response filtering), are analyzed. A novel reference frame generation approach, called "generalized source-channel prediction", is proposed, which involves infinite impulse response filtering. Experimental results show significant performance gains and substantiate the effectiveness of the proposed encoder optimization approaches.