Super Efficient Neural Network for Compression Artifacts Reduction and
  Super Resolution
Wen Ma Qiuwen Lou Arman Kazemi Julian Faraone Tariq Afzal
Video quality can suffer from limited internet speed while being streamed by
users. Compression artifacts start to appear when the bitrate decreases to
match the available bandwidth. Existing algorithms either focus on removing the
compression artifacts at the same video resolution, or on upscaling the video
resolution but not removing the artifacts. Super resolution-only approaches
will amplify the artifacts along with the details by default. We propose a
lightweight convolutional neural network (CNN)-based algorithm which
simultaneously performs artifacts reduction and super resolution (ARSR) by
enhancing the feature extraction layers and designing a custom training
dataset. The output of this neural network is evaluated for test streams
compressed at low bitrates using variable bitrate (VBR) encoding. The output
video quality shows a 4-6 increase in video multi-method assessment fusion
(VMAF) score compared to traditional interpolation upscaling approaches such as
Lanczos or Bicubic.