In most video platforms, such as Youtube, and TikTok, the played videos
usually have undergone multiple video encodings such as hardware encoding by
recording devices, software encoding by video editing apps, and single/multiple
video transcoding by video application servers. Previous works in compressed
video restoration typically assume the compression artifacts are caused by
one-time encoding. Thus, the derived solution usually does not work very well
in practice. In this paper, we propose a new method, temporal spatial auxiliary
network (TSAN), for transcoded video restoration. Our method considers the
unique traits between video encoding and transcoding, and we consider the
initial shallow encoded videos as the intermediate labels to assist the network
to conduct self-supervised attention training. In addition, we employ adjacent
multi-frame information and propose the temporal deformable alignment and
pyramidal spatial fusion for transcoded video restoration. The experimental
results demonstrate that the performance of the proposed method is superior to
that of the previous techniques. The code is available at
this https URL

本文提出了一种新的方法 TSAN 用于解决因多次视频编码和转码而出现的视频压缩失真问题，该方法使用浅层编码视频作为中间标签以辅助实现自监督注意力训练，并使用邻域多帧信息进行转码视频恢复。实验结果表明，所提出的方法的性能优于以往技术。