Video post-processing methods can improve the quality of compressed videos at the decoder side. Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos. However, in most cases, the quantization parameters of the decoded video are unknown. This makes existing methods have their limitations in improving video quality. To tackle this problem, this work proposes a diffusion model based post-processing method for compressed videos. The proposed method first estimates the feature vectors of the compressed video and then uses the estimated feature vectors as the prior information for the quality enhancement model to adaptively enhance the quality of compressed video with different quantization parameters. Experimental results show that the quality enhancement results of our proposed method on mixed datasets are superior to existing methods.

翻译：视频后处理方法可以在解码端提升压缩视频的质量。现有方法大多需要为使用不同量化参数的压缩视频训练相应模型，以改善压缩视频质量。然而，在大多数情况下，解码视频的量化参数是未知的，这使得现有方法在提升视频质量方面存在局限性。为解决这一问题，本文提出一种基于扩散模型的压缩视频后处理方法。该方法首先估计压缩视频的特征向量，然后将估计的特征向量作为质量增强模型的先验信息，自适应地增强不同量化参数下压缩视频的质量。实验结果表明，在混合数据集上，本方法的质量增强结果优于现有方法。