  Although the video compression ratio nowadays becomes higher, the video
coders such as H.264/AVC, H.265/HEVC, H.266/VVC always suffer from the video
artifacts. In this paper, we design a neural network to enhance the quality of
the compressed frame by leveraging the block information, called B-DRRN (Deep
Recursive Residual Network with Block information). Firstly, an extra network
branch is designed for leveraging the block information of the coding unit
(CU). Moreover, to avoid a great increase in the network size, Recursive
Residual structure and sharing weight techniques are applied. We also conduct a
new large-scale dataset with 209,152 training samples. Experimental results
show that the proposed B-DRRN can reduce 6.16% BD-rate compared to the HEVC
standard. After efficiently adding an extra network branch, this work can
improve the performance of the main network without increasing any memory for
storing.
