Rate-adaptive codes for distributed source coding
Signal Processing - Special section: Distributed source coding
Distributed Grayscale Stereo Image Coding with Unsupervised Learning of Disparity
DCC '07 Proceedings of the 2007 Data Compression Conference
Studying the GOP size impact on the performance of a feedback channel-based Wyner-Ziv video codec
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
The rate-distortion function for source coding with side information at the decoder
IEEE Transactions on Information Theory
Refining side information for improved transform domain Wyner-Ziv video coding
IEEE Transactions on Circuits and Systems for Video Technology
Perceptual-based distributed video coding
Journal of Visual Communication and Image Representation
Proceedings of the 20th ACM international conference on Multimedia
Video compression schemes using edge feature on wireless video sensor networks
Journal of Electrical and Computer Engineering
Progressively refined wyner-ziv video coding for visual sensors
ACM Transactions on Sensor Networks (TOSN)
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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.