  In this paper, a novel QP variable convolutional neural network based in-loop
filter is proposed for VVC intra coding. To avoid training and deploying
multiple networks, we develop an efficient QP attention module (QPAM) which can
capture compression noise levels for different QPs and emphasize meaningful
features along channel dimension. Then we embed QPAM into the residual block,
and based on it, we design a network architecture that is equipped with
controllability for different QPs. To make the proposed model focus more on
examples that have more compression artifacts or is hard to restore, a focal
mean square error (MSE) loss function is employed to fine tune the network.
Experimental results show that our approach achieves 4.03\% BD-Rate saving on
average for all intra configuration, which is even better than QP-separate CNN
models while having less model parameters.
