Abstract Video coding effectively reduces the amount of video data while unavoidably producing compression noise. Compression noise can cause significant artifacts in compressed video, such as blocking, ringing, and blurring, which seriously affects the visual quality of videos and the value of videos for content analysis. In compressed video quality enhancement, few methods based on deep learning fully consider the relationship between video content and compression noise or the possibility of uniting the encoder or the decoder to enhance the quality of compressed video. In an approach different from existing methods, we propose a video quality enhancement framework based on the distribution characteristics of compression noise. The proposed framework consists of two parts: at the encoder, we propose a convolutional neural network (CNN)-based in-loop filtering network combined with noise distribution (IFN-ND) characteristics for the I frame instead of high efficiency video coding (HEVC) standard in-loop filters; at the decoder, we propose a CNN-based quality enhancement network combined with the noise distribution characteristics (PQEN-ND) for the P frames. The noise characteristics are extracted from the code stream to further improve the performance of the proposed networks. The experiments show that the proposed method can significantly improve the quality of HEVC compressed video, achieving an average 12.84% reduction in the BD rate and up to a 1.0476 dB increase in the peak signal-to-noise ratio (PSNR).

中文翻译：

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一种用于高效视频编码的具有噪声分布特性的质量增强框架

摘要 视频编码有效地减少了视频数据量，同时不可避免地产生了压缩噪声。压缩噪声会在压缩视频中造成明显的伪像，如块、振铃、模糊等，严重影响视频的视觉质量和视频的内容分析价值。在压缩视频质量提升中，很少有基于深度学习的方法充分考虑视频内容与压缩噪声之间的关系或联合编码器或解码器来提升压缩视频质量的可能性。在与现有方法不同的方法中，我们提出了一种基于压缩噪声分布特征的视频质量增强框架。提议的框架由两部分组成：在编码器处，我们提出了一种基于卷积神经网络 (CNN) 的环路滤波网络，结合了 I 帧的噪声分布 (IFN-ND) 特性，而不是高效视频编码 (HEVC) 标准环路滤波器；在解码器，我们提出了一个基于 CNN 的质量增强网络，结合了 P 帧的噪声分布特性（PQEN-ND）。从码流中提取噪声特征以进一步提高所提出网络的性能。实验表明，所提出的方法可以显着提高HEVC压缩视频的质量，BD率平均降低12.84%，峰值信噪比（PSNR）提高1.0476 dB。