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.

网络速度限制会导致视频的质量下降并出现压缩伪影；本文提出了一种基于轻量级卷积神经网络（CNN）的算法，能够同时降低伪影并进行超分辨率重建，通过提升特征提取层和设计训练数据集来实现；通过对使用可变比特率编码进行压缩的测试视频评估，输出视频质量较传统插值方法（如 Lanczos 或 Bicubic）提高了 4-6 个 VMAF 分数。