Optimizing motion compensated prediction for error resilient video coding

  • Authors:

  • Hua Yang;Kenneth Rose

  • Affiliations:

  • Thomson Corporate Research, Princeton, NJ;Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2010

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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.