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Internet Video Delivery Improved by Super-Resolution with GAN

Author

Listed:
  • Joao da Mata Liborio

    (Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil
    Center for Higher Studies of Itacoatiara, Amazonas State University (UEA), Itacoatiara 69101-416, Brazil)

  • Cesar Melo

    (Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil)

  • Marcos Silva

    (Computing Institute, Federal University of Amazonas (UFAM), Manaus 69080-900, Brazil)

Abstract

In recent years, image and video super-resolution have gained attention outside the computer vision community due to the outstanding results produced by applying deep-learning models to solve the super-resolution problem. These models have been used to improve the quality of videos and images. In the last decade, video-streaming applications have also become popular. Consequently, they have generated traffic with an increasing quantity of data in network infrastructures, which continues to grow, e.g., global video traffic is forecast to increase from 75% in 2017 to 82% in 2022. In this paper, we leverage the power of deep-learning-based super-resolution methods and implement a model for video super-resolution, which we call VSRGAN+. We train our model with a dataset proposed to teach systems for high-level visual comprehension tasks. We also test it on a large-scale JND-based coded video quality dataset containing 220 video clips with four different resolutions. Additionally, we propose a cloud video-delivery framework that uses video super-resolution. According to our findings, the VSRGAN+ model can reconstruct videos without perceptual distinction of the ground truth. Using this model with added compression can decrease the quantity of data delivered to surrogate servers in a cloud video-delivery framework. The traffic decrease reaches 98.42% in total.

Suggested Citation

  • Joao da Mata Liborio & Cesar Melo & Marcos Silva, 2022. "Internet Video Delivery Improved by Super-Resolution with GAN," Future Internet, MDPI, vol. 14(12), pages 1-23, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:364-:d:995547
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    References listed on IDEAS

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    1. Jeff Hecht, 2016. "The bandwidth bottleneck that is throttling the Internet," Nature, Nature, vol. 536(7615), pages 139-142, August.
    2. Jia Dong & Quan Qian, 2022. "A Density-Based Random Forest for Imbalanced Data Classification," Future Internet, MDPI, vol. 14(3), pages 1-20, March.
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