VideoSet : A large-scale compressed video quality dataset based on JND measurement

Haiqiang WANG, Ioannis KATSAVOUNIDIS, Jiantong ZHOU, Jeonghoon PARK, Shawmin LEI, Xin ZHOU, Man-On PUN, Xin JIN, Ronggang WANG, Xu WANG, Yun ZHANG, Jiwu HUANG, Sam KWONG, C.-C. Jay KUO

Research output: Journal PublicationsJournal Article (refereed)peer-review

107 Citations (Scopus)


A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed in Lin et al. (2015). Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California in Jin et al. (2016) and Wang et al. (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920×1080,1280×720,960×540 and 640×360). For each of the 880 video clips, we encode it using the H.264/AVC codec with QP=1,…,51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (Wang et al., 2016 [4]).
Original languageEnglish
Pages (from-to)292-302
JournalJournal of Visual Communication and Image Representation
Publication statusPublished - 1 Jul 2017
Externally publishedYes

Bibliographical note

This research was funded by Netflix, Huawei, Samsung and MediaTek. The subjective tests were conducted in the City University of Hong Kong and five universities in the Shenzhen City of China. They were Shenzhen University, Chinese University of Hong Kong (SZ), Tsinghua University, Peking University and Chinese Academy of Sciences. Computation for the work was supported in part by the University of Southern California's Center for High-Performance Computing ( The authors would like to give thanks to these companies and universities for their strong support.


  • Human visual system (HVS)
  • Just noticeable difference (JND)
  • Video coding
  • Video quality


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