We study domain-specific video streaming. Specifically, we target a streaming
setting where the videos to be streamed from a server to a client are all in
the same domain and they have to be compressed to a small size for low-latency
transmission. Several popular video streaming services, such as the video game
streaming services of GeForce Now and Twitch, fall in this category. While
conventional video compression standards such as H.264 are commonly used for
this task, we hypothesize that one can leverage the property that the videos
are all in the same domain to achieve better video quality. Based on this
hypothesis, we propose a novel video compression pipeline. Specifically, we
first apply H.264 to compress domain-specific videos. We then train a novel
binary autoencoder to encode the leftover domain-specific residual information
frame-by-frame into binary representations. These binary representations are
then compressed and sent to the client together with the H.264 stream. In our
experiments, we show that our pipeline yields consistent gains over standard
H.264 compression across several benchmark datasets while using the same
channel bandwidth.Comment: Accepted in AAAI'18. Project website at
https://research.nvidia.com/publication/2018-02_Learning-Binary-Residua
