Video Compression Artifacts Removal with Efficient Non-local Block

Lossy video compression is widely applied in the process of video transmission and storage. With the pursuit of higher compression ratio, the demand for compressed video-enhancement is derived from the annoying degradation. Recently some deep-neural-network-based approaches have achieved impressive performance in this task. Unfortunately, most of them are plainly designed for images without exploiting the high temporal redundancy for video restoration. To this end, we present a two-stage Video compression Artifacts Removal Neural Network (VARNN) with deformable convolutional kernels and modified non-local blocks. Conventional non-local block is incapable of capturing dependencies among channels and suffers from high computation and memory cost. We thus introduce a Divided Non-Local Block (DNLB), in which global dependencies can be captured in a fast and low space-cost way. Finally, experiments show that the proposed VARNN outperforms some state-of-the-art methods.