Adaptive Predict Based on Fading Compensation for Lifting-Based Motion Compensated Temporal Filtering


A lifting implementation of the discrete wavelet transform applied along motion trajectories has recently gained a lot of attention in the video community as strong candidates in incoming scalable video coders. We generalize the coding scheme for classical lifting-based motion compensation temporal filtering and permit the codec to choose adaptively between the original reference frames and new fading-compensated reference frames to predict residuals while maintaining the invertibility of the inter-frame transform. Experimental results show that the proposed algorithm not only significantly improves subjective visual quality of the temporal low-pass frames, but also has 0.15-0.3 dB gain in PSNR performance compared with the normal (5, 3) lifting schemes.

Proceedings. (ICASSP ‘05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.