CNN-based super-resolution adapted to quantization parameters

Toshiya Hori, Zichen Gong, Hiroshi Watanabe, Tomohiro Ikai, Takeshi Chujoh, Eiichi Sasaki, Norio Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In video transmission, the videos are encoded and decoded. At that time, bit control is performed by specifying the quantization parameter (QP). The video undergoes various processing to remove redundancy and then orthogonally transforms the video signal into the frequency domain. The frequency domain coefficients are then quantized and transmitted. At that time, by specifying QP, the quantization step is changed, and the amount of data can be changed. In an opinion, a codec using super-resolution is proposed. At the CNN based super-resolution of encoded images, the degradation of the input image due to encoding depends on the characteristics of the image. As a result, there is a problem that the weights of the optimal CNN for the input image changes depending on the image characteristics. In order to solve this problem, we propose a method to adaptively perform super-resolution corresponding to image degradation.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2020
EditorsPhooi Yee Lau, Mohammad Shobri
ISBN (Electronic)9781510638358
Publication statusPublished - 2020
EventInternational Workshop on Advanced Imaging Technology, IWAIT 2020 - Yogyakarta, Indonesia
Duration: 2020 Jan 52020 Jan 7

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Workshop on Advanced Imaging Technology, IWAIT 2020


  • Quantization Parameter
  • Super-resolution
  • Video Coding

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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