In recent years, video compression techniques have been significantly challenged by the rapidly increased demands associated with high quality and immersive video content. Among various compression tools, post-processing can be applied on reconstructed video content to mitigate visible compression artefacts and to enhance overall perceptual quality. Inspired by advances in deep learning, we propose a new CNN-based post-processing approach, which has been integrated with two state-of-the-art coding standards, VVC and AV1. The results show consistent coding gains on all tested sequences at various spatial resolutions, with average bit rate savings of 4.0% and 5.8% against original VVC and AV1 respectively (based on the assessment of PSNR). This network has also been trained with perceptually inspired loss functions, which have further improved reconstruction quality based on perceptual quality assessment (VMAF), with average coding gains of 13.9% over VVC and 10.5% against AV1.

中文翻译：

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使用基于 CNN 的后处理进行视频压缩

近年来，与高质量和身临其境的视频内容相关的快速增长的需求对视频压缩技术提出了重大挑战。在各种压缩工具中，后处理可以应用于重建的视频内容，以减轻可见的压缩伪影并提高整体感知质量。受深度学习进步的启发，我们提出了一种新的基于 CNN 的后处理方法，该方法已与两个最先进的编码标准 VVC 和 AV1 集成。结果显示，所有测试序列在不同空间分辨率下的编码增益一致，与原始 VVC 和 AV1 相比，平均比特率分别节省 4.0% 和 5.8%（基于对 PSNR 的评估）。该网络还接受了受感知启发的损失函数的训练，