  Compressed videos constitute 70% of Internet traffic, and video upload growth
rates far outpace compute and storage improvement trends. Past work in
leveraging perceptual cues like saliency, i.e., regions where viewers focus
their perceptual attention, reduces compressed video size while maintaining
perceptual quality, but requires significant changes to video codecs and
ignores the data management of this perceptual information.
  In this paper, we propose Vignette, a compression technique and storage
manager for perception-based video compression. Vignette complements
off-the-shelf compression software and hardware codec implementations.
Vignette's compression technique uses a neural network to predict saliency
information used during transcoding, and its storage manager integrates
perceptual information into the video storage system to support a perceptual
compression feedback loop. Vignette's saliency-based optimizations reduce
storage by up to 95% with minimal quality loss, and Vignette videos lead to
power savings of 50% on mobile phones during video playback. Our results
demonstrate the benefit of embedding information about the human visual system
into the architecture of video storage systems.
