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Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12359)

Abstract

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.

Keywords

Video perceptual quality enhancement Wavelet packet transform GAN 

Notes

Acknowledgement

This work was supported by the NSFC under Project 61876013, Project 61922009, and Project 61573037.

Supplementary material

504468_1_En_24_MOESM1_ESM.pdf (611 kb)
Supplementary material 1 (pdf 610 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeihang UniversityBeijingChina
  2. 2.School of Cyber Science and TechnologyBeihang UniversityBeijingChina
  3. 3.College of SoftwareBeihang UniversityBeijingChina
  4. 4.Hangzhou Innovation InstituteBeihang UniversityZhejiangChina
  5. 5.Department of Computer ScienceUniversity of OxfordOxfordUK

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