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.

中文翻译：

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基于高效QP变量卷积神经网络的环路内编码滤波器

本文针对VVC帧内编码提出了一种基于QP变量卷积神经网络的环路滤波器。为了避免训练和部署多个网络，我们开发了一个有效的QP注意模块（QPAM），该模块可以捕获不同QP的压缩噪声水平并强调沿通道维度的有意义的功能。然后，我们将QPAM嵌入到剩余块中，并在此基础上设计一种网络架构，该架构具有针对不同QP的可控性。为了使提出的模型更多地关注具有更多压缩伪影或难以恢复的示例，采用了均方根误差（MSE）损失函数来微调网络。实验结果表明，对于所有内部配置，我们的方法平均可节省4.03％的BD-Rate，