  Video compression is a critical component of Internet video delivery. Recent
work has shown that deep learning techniques can rival or outperform
human-designed algorithms, but these methods are significantly less compute and
power-efficient than existing codecs. This paper presents a new approach that
augments existing codecs with a small, content-adaptive super-resolution model
that significantly boosts video quality. Our method, SRVC, encodes video into
two bitstreams: (i) a content stream, produced by compressing downsampled
low-resolution video with the existing codec, (ii) a model stream, which
encodes periodic updates to a lightweight super-resolution neural network
customized for short segments of the video. SRVC decodes the video by passing
the decompressed low-resolution video frames through the (time-varying)
super-resolution model to reconstruct high-resolution video frames. Our results
show that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of
H.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep
learning-based video compression scheme. SRVC runs at 90 frames per second on a
NVIDIA V100 GPU.
