Abstract
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 language | English |
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Pages (from-to) | 292-302 |
Number of pages | 11 |
Journal | Journal of Visual Communication and Image Representation |
Volume | 46 |
Early online date | 28 Apr 2017 |
DOIs | |
Publication status | Published - Jul 2017 |
Externally published | Yes |
Bibliographical note
Publisher Copyright: © 2017Funding
This research was funded by Netflix, Huawei, Samsung and MediaTek.
Keywords
- Human visual system (HVS)
- Just noticeable difference (JND)
- Video coding
- Video quality