A JND Dataset Based on VVC Compressed Images

Xuelin SHEN, Zhangkai NI, Wenhan YANG, Xinfeng ZHANG, Shiqi WANG, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)


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)
Number of pages6
ISBN (Electronic)9781728114859
ISBN (Print)9781728114866
Publication statusPublished - Jul 2020
Externally publishedYes
Event2020 IEEE International Conference on Multimedia and Expo (ICME 2020) - Virtual, London, United Kingdom
Duration: 6 Jul 202010 Jul 2020


Conference2020 IEEE International Conference on Multimedia and Expo (ICME 2020)
Country/TerritoryUnited Kingdom
Internet address

Bibliographical note

This work was supported by GRF - RGC General Research Fund 9042322( CityU 11200116), 9042489 (CityU 11206317) and 9042816 (C-ityU 11209819).


  • Just noticeable distortion
  • Visual perception


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