  This work focuses on low bitrate video streaming scenarios (e.g. 50 -
200Kbps) where the video quality is severely compromised. We present a family
of novel deep generative models for enhancing perceptual video quality of such
streams by performing super-resolution while also removing compression
artifacts. Our model, which we call SuperTran, consumes as input a single
high-quality, high-resolution reference images in addition to the low-quality,
low-resolution video stream. The model thus learns how to borrow or copy visual
elements like textures from the reference image and fill in the remaining
details from the low resolution stream in order to produce perceptually
enhanced output video. The reference frame can be sent once at the start of the
video session or be retrieved from a gallery. Importantly, the resulting output
has substantially better detail than what has been otherwise possible with
methods that only use a low resolution input such as the SuperVEGAN method.
SuperTran works in real-time (up to 30 frames/sec) on the cloud alongside
standard pipelines.
