  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 (i.e., 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 unified
polynomial functions. 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 is the first end-to-end learned video compression method
that outperforms the latest VVC/H.266 standard reference software in terms of
MS-SSIM.
