Wyner-Ziv coding of video with unsupervised motion vector learning

Distributed source coding theory has long promised a new method of encoding video that is much lower in complexity than conventional methods. In the distributed framework, the decoder is tasked with exploiting the redundancy of the video signal. Among the difficulties in realizing a practical codec has been the problem of motion estimation at the decoder. In this paper, we propose a technique for unsupervised learning of forward motion vectors during the decoding of a frame with reference to its previous reconstructed frame. The technique, described for both pixel-domain and transform-domain coding, is an instance of the expectation maximization algorithm. The performance of our transform-domain motion learning video codec improves as GOP size grows. It is better than using motion-compensated temporal interpolation by 0.5dB when GOP size is 2, and by even more when GOP size is larger. It performs within about 0.25dB of a codec that knows the motion vectors through an oracle, but is hundreds of orders of magnitude less complex than a corresponding brute-force decoder motion search approach would be.