Versatile Learned Video Compression

Runsen Feng, Zongyu Guo, Zhizheng Zhang, Zhibo Chen

Keywords: Video Compression, Deep Learning

TL;DR: Versatile Learned Video Compression

Abstract: Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned video compression schemes are limited by the binding of the prediction mode and the fixed network framework. They are unable to support various inter prediction modes and thus inapplicable for various scenarios.In this paper, to break this limitation, we propose a versatile learned video compression (VLVC) framework that uses one model to support all possible prediction modes.Specifically, to realize versatile compression, we first build a motion compensation module that applies multiple 3D motion vector fields (\ieno, voxel flows) for weighted trilinear warping in spatial-temporal space. The voxel flows convey the information of temporal reference position that helps to decouple inter prediction modes away from framework designing.Secondly, in case of multiple-reference-frame prediction, we apply a flow prediction module to predict accurate motion trajectories with a unified polynomial function. We show that the flow prediction module can largely reduce the transmission cost of voxel flows.Experimental results demonstrate that our proposed VLVC not only supports versatile compression in various settings but also achieves comparable R-D performance with the latest Versatile Video Coding (VVC) standard in terms of MS-SSIM.

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