论文标题
一个有效的QP变量卷积神经网络基于环内编码的环内滤波器
An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding
论文作者
论文摘要
在本文中，提出了一种新型QP变量卷积神经网络内部滤波器的滤波器，用于VVC内部编码。为了避免培训和部署多个网络，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QPS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嵌入残差块中，并基于它，我们设计了一个具有可控性的网络体系结构。为了使所提出的模型更多地集中在具有更多压缩工件或难以恢复的示例上，采用焦点均值误差（MSE）损耗函数来微调网络。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 \％BD速率，这甚至比QP分离CNN模型更好，而模型参数较少。
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.
