A Diffusion Model Based Quality Enhancement Method for HEVC Compressed
  Video
Zheng Liu Honggang Qi
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.