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.

翻译：视频流媒体传输受限于网络带宽时，用户端视频质量会显著下降。当比特率降低以匹配可用带宽时，压缩伪影开始出现。现有算法通常仅针对相同分辨率下的压缩伪影去除，或仅关注视频分辨率提升而忽略伪影处理。单纯超分辨率方法会默认在放大细节的同时放大伪影。我们提出一种轻量级卷积神经网络（CNN）算法，通过改进特征提取层并设计定制训练数据集，同步实现伪影抑制与超分辨率（ARSR）。该神经网络的输出结果针对采用可变比特率（VBR）编码的低比特率压缩测试流进行评估。与Lanczos或Bicubic等传统插值放大方法相比，输出视频质量在视频多方法评估融合（VMAF）分数上提升4-6分。