Multi-Gradient Convolutional Neural Network Based In-Loop Filter for VVC

While recent researches on convolutional neural network
(CNN) based in-loop filters for High Efficiency Video Coding
(HEVC) have achieved great success, the performance
of these models on the new standard Versatile Video Coding
(VVC) may degrade due to many novel adopted techniques
which make the compression process more fine and capture more
image details. In this work, the performances on VVC of
two CNN based in-loop filters proposed for HEVC are investigated
and a multi-gradient convolutional neural network
based in-loop filter (MGNLF) for VVC is proposed. The proposed
model exploits the divergence and second derivative of
frame, which contain much potential image structural information,
like contour information, to restore more detail information
to further improve the quality of frames. Experimental
results demonstrate our approach can significantly improve
the coding performance. On average, 3.29% BD-Rate
reduction is achieved on luma component under all intra configuration
compared with the original VVC with DBF and
SAO enabled, which also outperforms other state-of-the-art
approaches for VVC.