  Convolutional Neural Network (CNN) and Transformer have attracted much
attention recently for video post-processing (VPP). However, the interaction
between CNN and Transformer in existing VPP methods is not fully explored,
leading to inefficient communication between the local and global extracted
features. In this paper, we explore the interaction between CNN and Transformer
in the task of VPP, and propose a novel Spatial and Channel Hybrid-Attention
Video Post-Processing Network (SC-HVPPNet), which can cooperatively exploit the
image priors in both spatial and channel domains. Specifically, in the spatial
domain, a novel spatial attention fusion module is designed, in which two
attention weights are generated to fuse the local and global representations
collaboratively. In the channel domain, a novel channel attention fusion module
is developed, which can blend the deep representations at the channel dimension
dynamically. Extensive experiments show that SC-HVPPNet notably boosts video
restoration quality, with average bitrate savings of 5.29%, 12.42%, and 13.09%
for Y, U, and V components in the VTM-11.0-NNVC RA configuration.
