  We propose a video compression framework using conditional Generative
Adversarial Networks (GANs). We rely on two encoders: one that deploys a
standard video codec and another which generates low-level maps via a pipeline
of down-sampling, a newly devised soft edge detector, and a novel lossless
compression scheme. For decoding, we use a standard video decoder as well as a
neural network based one, which is trained using a conditional GAN. Recent
"deep" approaches to video compression require multiple videos to pre-train
generative networks to conduct interpolation. In contrast to this prior work,
our scheme trains a generative decoder on pairs of a very limited number of key
frames taken from a single video and corresponding low-level maps. The trained
decoder produces reconstructed frames relying on a guidance of low-level maps,
without any interpolation. Experiments on a diverse set of 131 videos
demonstrate that our proposed GAN-based compression engine achieves much higher
quality reconstructions at very low bitrates than prevailing standard codecs
such as H.264 or HEVC.
