  Stereoscopic video conferencing is still challenging due to the need to
compress stereo RGB-D video in real-time. Though hardware implementations of
standard video codecs such as H.264 / AVC and HEVC are widely available, they
are not designed for stereoscopic videos and suffer from reduced quality and
performance. Specific multiview or 3D extensions of these codecs are complex
and lack efficient implementations. In this paper, we propose a new approach to
upgrade a 2D video codec to support stereo RGB-D video compression, by wrapping
it with a neural pre- and post-processor pair. The neural networks are
end-to-end trained with an image codec proxy, and shown to work with a more
sophisticated video codec. We also propose a geometry-aware loss function to
improve rendering quality. We train the neural pre- and post-processors on a
synthetic 4D people dataset, and evaluate it on both synthetic and
real-captured stereo RGB-D videos. Experimental results show that the neural
networks generalize well to unseen data and work out-of-box with various video
codecs. Our approach saves about 30% bit-rate compared to a conventional video
coding scheme and MV-HEVC at the same level of rendering quality from a novel
view, without the need of a task-specific hardware upgrade.
