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

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Computer Vision – ECCV 2020 (ECCV 2020)

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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.

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Acknowledgement

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

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Beihang University, Beijing, China

    Jianyi Wang & Mai Xu

  2. School of Cyber Science and Technology, Beihang University, Beijing, China

    Xin Deng

  3. College of Software, Beihang University, Beijing, China

    Congyong Chen

  4. Hangzhou Innovation Institute, Beihang University, Zhejiang, China

    Mai Xu

  5. Department of Computer Science, University of Oxford, Oxford, UK

    Yuhang Song

Authors
  1. Jianyi Wang
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  2. Xin Deng
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  3. Mai Xu
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  4. Congyong Chen
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  5. Yuhang Song
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Corresponding author

Correspondence to Mai Xu .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Wang, J., Deng, X., Xu, M., Chen, C., Song, Y. (2020). Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-58568-6_24

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