LIVE Wild Compressed Picture Quality Database
Description
Current mainstream image quality databases, such as LIVE IQA, TID2013, and CSIQ, are widely used in IQA research. The LIVE IQA Database, which contains 29 reference images and 779 distorted images of five distortion types, was the first large public-domain IQA database. TID2013, which extends TID2008, contains 3000 images with 24 different kinds of distortions. CSIQ contains 30 original images, each distorted by one of six different types of distortions. These major databases have largely support the development of modern IQA algorithms over the past 15 years. However, since they all make use of high quality pristine images as reference images, these databases fail to reflect the influence of possible degraded quality of reference images on reference quality prediction performance.
A recently published database, called the LIVE In the Wild Challenge IQA Database, contains more than 1100 authentically distorted images captured by a wide variety of mobile devices. The distortions in it are representative of those encountered in practical consumer applications, where the images produced by uncertain amateur hands are often of reduced quality. Towards the development of algorithms that can assess the overall quality of these kinds of image after they are also compressed, we have created a new database we call the LIVE Wild Compressed Picture Quality Database, which uses degraded authentic images from as reference images.
A total of 80 images were chosen from to serve as references in the LIVE Wild Compressed Picture Quality Database. These authentic reference images contain numerous types and complex combinations of in-capture distortions such as blur, over/under-exposure, lighting etc. The reference images were then JPEG compressed using the Matlab JPEG tool into four different, broadly distinguishable severity levels. For each content, there are four compressed versions, yielding 320 compressed images.
Investigators
Xiangxu Yu - Graduate Student - email: yuxiangxu@utexas.edu
Christos G. Bampis - Software Engineer at Netflix - email: cbampis@gmail.com
Praful Gupta - Graduate Student - email: praful_gupta@utexas.edu
Alan Bovik
- Professor - email:
bovik@ece.utexas.edu
Download
We are making the LIVE Wild Compressed Picture Quality 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, C. G. Bampis, P. Gupta and A. C. Bovik, "Predicting the Quality of Images Compressed After Distortion in Two Steps", IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 5757-5770, December 2019. [paper]
- X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik, "Predicting the Quality of Images Compressed After Distortion in Two Steps", Proc. SPIE 10752, Applications of Digital Image Processing XLI, September 2018. [paper]
- X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik, "LIVE Wild Compressed Picture Quality Database", Online: http://live.ece.utexas.edu/research/twostep/index.html, 2018.
You can download the publicly available images together with the subjective data by clicking THIS link. Please fill THIS FORM and the password will be sent to you.
Copyright Notice
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Copyright (c) 2018 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, C. G. Bampis, P. Gupta and A. C. Bovik, "Predicting the Quality of Images Compressed After Distortion in Two Steps", IEEE Transactions on Image Processing, vol. 28, no. 12, pp. 5757-5770, December 2019. [paper]
- X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik, "Predicting the Quality of Images Compressed After Distortion in Two Steps", Proc. SPIE 10752, Applications of Digital Image Processing XLI, September 2018. [paper]
- X. Yu, C. G. Bampis, P. Gupta and A. C. Bovik, "LIVE Wild Compressed Picture Quality Database", Online: http://live.ece.utexas.edu/research/twostep/index.html, 2018.
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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