  In this paper we propose a generative adversarial network (GAN) framework to
enhance the perceptual quality of compressed videos. Our framework includes
attention and adaptation to different quantization parameters (QPs) in a single
model. The attention module exploits global receptive fields that can capture
and align long-range correlations between consecutive frames, which can be
beneficial for enhancing perceptual quality of videos. The frame to be enhanced
is fed into the deep network together with its neighboring frames, and in the
first stage features at different depths are extracted. Then extracted features
are fed into attention blocks to explore global temporal correlations, followed
by a series of upsampling and convolution layers. Finally, the resulting
features are processed by the QP-conditional adaptation module which leverages
the corresponding QP information. In this way, a single model can be used to
enhance adaptively to various QPs without requiring multiple models specific
for every QP value, while having similar performance. Experimental results
demonstrate the superior performance of the proposed PeQuENet compared with the
state-of-the-art compressed video quality enhancement algorithms.
