  The past few years have witnessed fast development in video quality
enhancement via deep learning. Existing methods mainly focus on enhancing the
objective quality of compressed video while ignoring its perceptual quality. In
this paper, we focus on enhancing the perceptual quality of compressed video.
Our main observation is that enhancing the perceptual quality mostly relies on
recovering high-frequency sub-bands in wavelet domain. Accordingly, we propose
a novel generative adversarial network (GAN) based on multi-level wavelet
packet transform (WPT) to enhance the perceptual quality of compressed video,
which is called multi-level wavelet-based GAN (MW-GAN). In MW-GAN, we first
apply motion compensation with a pyramid architecture to obtain temporal
information. Then, we propose a wavelet reconstruction network with
wavelet-dense residual blocks (WDRB) to recover the high-frequency details. In
addition, the adversarial loss of MW-GAN is added via WPT to further encourage
high-frequency details recovery for video frames. Experimental results
demonstrate the superiority of our method.
