Welcome to the LIVE HDR Video Quality Assessment Database
LIVE HDR Video Quality Assessment Database
Introduction
High Dynamic Range (HDR) videos can represent a much greater range of brightness and color than Standard Dynamic Range (SDR) videos and are rapidly becoming an industry standard. HDR videos have more challenging capture, transmission, and display requirements than legacy SDR videos. With their greater bit depth, advanced electro-optical transfer functions, and wider color gamuts, comes the need for video quality algorithms that are specifically designed to predict the quality of HDR videos. Towards this end, we present the first publicly released large-scale subjective study of HDR videos. We study the effect of distortions such as compression and aliasing on the quality of HDR videos. We also study the effect of ambient illumination on perceptual quality of HDR videos by conducting the study in both a dark lab environment and a brighter living-room environment. A total of 66 subjects participated in the study and more than 20,000 opinion scores were collected, which makes this the largest in-lab study of HDR video quality ever. We anticipate that the dataset will be a valuable resource for researchers to develop better models of perceptual quality for HDR videos.
Download
We are making the LIVE HDR Video Quality Assessment Database available to the research community free of charge. If you use this database in your research, we kindly ask that you to cite our paper listed below:
- Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethu- raman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022
You can download the publicly available release of the database by filling THIS form. The password and link to the database will be sent to you once you complete the form.
Database Description
LIVE HDR database includes 310 videos of 31 contents at 10 bitrates and resolutions. We performed a subjective quality study using the new database, whereby more than 20,000 human opinions were gathered from 40 subjects. All videos were HDR10, and they have frame rates 50 fps and 60 fps. We clipped all the video sequences into one or more clips of 7-10 sec.
Investigators
The investigators in this research are:
- Zaixi Shang ( zxshang@utexas.edu ) -- Graduate student, Dept. of ECE, UT Austin.
- Joshua Ebenezer ( joshuaebenezer@utexas.edu ) -- Graduate student, Dept. of ECE, UT Austin.
- Yongjun Wu ( yongjuw@amazon.com ) Amazon Prime Video, Seattle, WA
- Hai Wei ( haiwei@amazon.com ) Amazon Prime Video, Seattle, WA
- Sriram Sethuraman ( sssethur@amazon.com ) Amazon Prime Video, Seattle, WA
- Alan C. Bovik ( bovik@ece.utexas.edu ) -- Professor, Dept. of ECE, UT Austin
Copyright Notice
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Copyright (c) 2016 The University of Texas at Austin
All rights reserved.
Permission is hereby granted, without written agreement and without license or royalty fees, to
use, copy, modify, and distribute this database (the videos, the results and the source files)
and its documentation for any purpose, provided that the copyright notice in its entirety appear
in all copies of this database, and the original source of this database, Laboratory for Image
and Video Engineering (LIVE,
http://live.ece.utexas.edu
) at the University of Texas at Austin (UT Austin,
http://www.utexas.edu
), is acknowledged in any publication that reports research using this database.
The following papers are to be cited in the bibliography whenever the database is used as:
- Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethu- raman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022
IN NO EVENT SHALL THE UNIVERSITY OF TEXAS AT AUSTIN BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT,
SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THIS DATABASE AND ITS
DOCUMENTATION, EVEN IF THE UNIVERSITY OF TEXAS AT AUSTIN HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGE.
THE UNIVERSITY OF TEXAS AT AUSTIN SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE
DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF TEXAS AT AUSTIN HAS NO
OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.
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