Learning-based multiview video coding

Baochun Bai, Li Cheng, Cheng Lei, Pierre Boulanger, Janelle J. Harms

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.