LIVE YouTube High Frame Rate (LIVE-YT-HFR) Database
Description
High frame rate (HFR) videos are becoming increasingly common with the tremendous popularity of live, high-action streaming content such as sports. Although HFR contents are generally of very high quality, high bandwidth requirements make them challenging to deliver efficiently, while simultaneously maintaining their quality. To optimize trade-offs between bandwidth requirements and video quality, in terms of frame rate adaptation, it is imperative to understand the intricate relationship between frame rate and perceptual video quality. Towards advancing progression in this direction we designed a new subjective resource, called the LIVE-YouTube-HFR (LIVE-YT-HFR) Dataset, which is comprised of 480 videos having 6 different frame rates, obtained from 16 diverse contents. In order to understand the combined effects of compression and frame rate adjustment, we also processed videos at 5 compression levels at each frame rate. To obtain subjective labels on the videos, we conducted a human study yielding 19,000 human quality ratings obtained from a pool of 85 human subjects.
Investigators
Pavan Chennagiri - Graduate Student - email: pavancm@utexas.edu
Xiangxu Yu - Graduate Student - email: yuxiangxu@utexas.edu
Neil Birkbeck - Google - email: birkbeck@google.com
Yilin Wang - Google - email: yilin@google.com
Balu Adsumilli - Google - email: badsumilli@google.com
Alan Bovik
- Professor - email:
bovik@ece.utexas.edu
Download
We are making the LIVE-YT-HFR 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:
- P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Subjective and Objective Quality Assessment of High Frame Rate Videos", submitted to IEEE Transactions on Image Processing, 2020 [paper]
- P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Capturing Video Frame Rate Variations through Entropic Differencing", arXiv preprint arXiv:2006.11424, 2020
You can download the publicly available videos together with the subjective data HERE.
Please fill THIS FORM and the password will be mailed to you.
Copyright Notice
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Copyright (c) 2020 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:
- P. C. Madhusudana, X. Yu, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Subjective and Objective Quality Assessment of High Frame Rate Videos", submitted to IEEE Transactions on Image Processing, 2020 [paper]
- P. C. Madhusudana, N. Birkbeck, Y. Wang, B. Adsumilli and A. C. Bovik, "Capturing Video Frame Rate Variations through Entropic Differencing", arXiv preprint arXiv:2006.11424, 2020
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