LIVE-YouTube Gaming Video Quality Database
Description
The rising popularity of online User-Generated-Content (UGC) in the form of streamed and shared videos, has hastened the development of perceptual Video Quality Assessment (VQA) models, which can be used to help optimize their delivery. Gaming videos, which are a relatively new type of UGC videos, are created when skilled gamers post videos of their gameplay. These kinds of screenshots of UGC gameplay videos have become extremely popular on major streaming platforms like YouTube and Twitch. Synthetically-generated gaming content presents challenges to existing VQA algorithms, including those based on natural scene/video statistics models. Synthetically generated gaming content presents different statistical behavior than naturalistic videos. A number of studies have been directed towards understanding the perceptual characteristics of professionally generated gaming videos arising in gaming video streaming, online gaming, and cloud gaming. However, little work has been done on understanding the quality of UGC gaming videos, and how it can be characterized and predicted. Towards boosting the progress of gaming video VQA model development, we conducted a comprehensive study of subjective and objective VQA models on UGC gaming videos. To do this, we created a novel UGC gaming video resource, called the LIVE-YouTube Gaming video quality (LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We conducted a subjective human study on this data, yielding 18,600 human quality ratings recorded by 61 human subjects.
Investigators
Xiangxu Yu - Graduate Student - email: yuxiangxu@utexas.edu
Zhenqiang Ying - Graduate Student - email: zqying@utexas.edu
Neil Birkbeck - Software Engineer at Google - email: birkbeck@google.com
Yilin Wang - Software Engineer at Google - email: yilin@google.com
Balu Adsumilli - Manager at Google - email: badsumilli@google.com
Alan Bovik
- Professor - email:
bovik@ece.utexas.edu
Download
We are making the LIVE-YouTube Gaming Video Quality (LIVE-YT-Gaming) Database available to the research community free of charge. If you use this database in your research, we kindly ask that you reference our papers listed below:
- X. Yu, Z. Ying, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Subjective and Objective Analysis of Streamed Gaming Videos", [paper]
You can download the publicly available videos together with the subjective data. Please fill THIS FORM and the downloading link will be sent to you.
Copyright Notice
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Copyright (c) 2021 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 images, 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:
- X. Yu, Z. Ying, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Subjective and Objective Analysis of Streamed Gaming Videos", [paper]
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.
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