  Video compression is a central feature of the modern internet powering
technologies from social media to video conferencing. While video compression
continues to mature, for many compression settings, quality loss is still
noticeable. These settings nevertheless have important applications to the
efficient transmission of videos over bandwidth constrained or otherwise
unstable connections. In this work, we develop a deep learning architecture
capable of restoring detail to compressed videos which leverages the underlying
structure and motion information embedded in the video bitstream. We show that
this improves restoration accuracy compared to prior compression correction
methods and is competitive when compared with recent deep-learning-based video
compression methods on rate-distortion while achieving higher throughput.
Furthermore, we condition our model on quantization data which is readily
available in the bitstream. This allows our single model to handle a variety of
different compression quality settings which required an ensemble of models in
prior work.
