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Video post-processing methods can improve the quality of compressed videos atthe decoder side. Most of the existing methods need to train correspondingmodels for compressed videos with different quantization parameters to improvethe quality of compressed videos. However, in most cases, the quantizationparameters of the decoded video are unknown. This makes existing methods havetheir limitations in improving video quality. To tackle this problem, this workproposes a diffusion model based post-processing method for compressed videos.The proposed method first estimates the feature vectors of the compressed videoand then uses the estimated feature vectors as the prior information for thequality enhancement model to adaptively enhance the quality of compressed videowith different quantization parameters. Experimental results show that thequality enhancement results of our proposed method on mixed datasets aresuperior to existing methods.

A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video