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Wyner-Ziv coding of video with unsupervised motion vector learning
Stanford University.
Stanford University.
KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0002-7807-5681
Stanford University.
2008 (English)In: Signal processing. Image communication, ISSN 0923-5965, E-ISSN 1879-2677, Vol. 23, no 5, 369-378 p.Article in journal (Refereed) Published
Abstract [en]

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 0.5 dB 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.

Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 23, no 5, 369-378 p.
Keyword [en]
Wyner-Ziv video coding, expectation maximization
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Research subject
URN: urn:nbn:se:kth:diva-93778DOI: 10.1016/j.image.2008.04.009ISI: 000257519500004OAI: diva2:523830

QC 20120427

Available from: 2012-04-26 Created: 2012-04-26 Last updated: 2016-04-20Bibliographically approved

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Flierl, Markus
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