Learning-based multiview video coding

Authors:
Baochun Bai;Li Cheng;Cheng Lei;Pierre Boulanger;Janelle Harms
Affiliations:
Department of Computing Science, University of Alberta, Edmonton, Canada;Toyota Technological Institute at Chicago, Chicago, Illinois;Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada;Department of Computing Science, University of Alberta, Edmonton, Canada
Venue:
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Year:
2009

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Abstract

In the past decade, machine learning techniques have made great progress. Inspired by the recent advancement on semi-supervised learning techniques, we propose a novel learning-based multiview video compression framework. Our scheme can efficiently compress the multiview video represented by multiview-video-plus-depth (MVD) format.We model the multiview video compression problem as a semi-supervised learning problem and design sophisticated mechanisms to achieve high compression efficiency. Our approach is significantly different from the traditional hybrid coding scheme such as H.264-based multiview video coding methods. The preliminary results show promising compression performance.