CPGA: Coding Priors-Guided Aggregation Network for Compressed Video
  Quality Enhancement
Qiang Zhu Jinhua Hao Yukang Ding Yu Liu Qiao Mo Ming Sun Chao Zhou Shuyuan Zhu
Recently, numerous approaches have achieved notable success in compressed
video quality enhancement (VQE). However, these methods usually ignore the
utilization of valuable coding priors inherently embedded in compressed videos,
such as motion vectors and residual frames, which carry abundant temporal and
spatial information. To remedy this problem, we propose the Coding
Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial
information from coding priors. The CPGA mainly consists of an inter-frame
temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA)
module. Specifically, the ITA module aggregates temporal information from
consecutive frames and coding priors, while the MNA module globally captures
spatial information guided by residual frames. In addition, to facilitate
research in VQE task, we newly construct the Video Coding Priors (VCP) dataset,
comprising 300 videos with various coding priors extracted from corresponding
bitstreams. It remedies the shortage of previous datasets on the lack of coding
information. Experimental results demonstrate the superiority of our method
compared to existing state-of-the-art methods. The code and dataset will be
released at https://github.com/CPGA/CPGA.git.