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A JND Dataset Based on VVC Compressed Images

  • Xuelin Shen
  • , Zhangkai Ni
  • , Wenhan Yang
  • , Xinfeng Zhang
  • , Shiqi Wang
  • , Sam Kwong
  • City University of Hong Kong
  • University of Chinese Academic of Sciences

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we establish a just noticeable distortion (JND) dataset based on the next generation video coding standard Versatile Video Coding (VVC). The dataset consists of 202 images which cover a wide range of content with resolution 1920×1080. Each image is encoded by VTM 5.0 intra coding with the quantization parameter (QP) ranging from 13 to 51. The details regarding dataset construction, subjective testing and data post-processing are described in this paper. Finally, the significance of the dataset towards future video coding research is envisioned. All source images as well as the testing data have been made available to the public.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728114859
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

Name2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020

Conference

Conference2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
Country/TerritoryUnited Kingdom
CityLondon
Period6/07/2010/07/20

Keywords

  • Just noticeable distortion
  • Visual perception

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