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Laboratory for Image & Video Engineering

Welcome to the LIVE Public-Domain Subjective In the Wild Image Quality Challenge Database

LIVE In the Wild Image Quality Challenge Database

Introduction

Image quality assessment (IQA) databases enable researchers to evaluate the performance of IQA algorithms and contribute towards attaining the ultimate goal of objective quality assessment research - matching human perception. Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. Our newly designed and created LIVE In the Wild Image Quality Challenge Database, contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment subjective study. The LIVE In the Wild Image Quality Database has over 350,000 opinion scores on 1,162 images evaluated by over 8100 unique human observers.

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We are making the LIVE In the Wild Image Quality Challenge 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:

a You can download the database by clicking THIS link. Please fill THIS FORM and the password will be sent to you.

Database Description

The goal of our study was to develop a database of authentically distorted images that will overcome the limitations of all the existing benchmark IQA databases and also challenge automatic IQA algorithms. The LIVE In the Wild Image Quality Challenge Database contains 1162 authentically distorted images captured from many diverse mobile devices. Each image was collected without artificially introducing any distortions beyond those occurring during capture, processing, and storage by a user’s device. We implemented an extensive online subjective study by leveraging Amazon’s crowdsourcing system, the Mechanical Turk to conduct a very large-scale, multi-month image quality assessment subjective study, wherein a wide range of diverse observers recorded their judgments of image quality. Each image was viewed and rated online on a continuous quality scale by an average of 175 unique subjects. The mean and variance of the Mean Opinion Scores (MOS) obtained from the subjective evaluations, along with the images, are available as part of the database. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrated excellent internal consistency of the subjective scores and presented the relevant results in our paper. We have also evaluated several top-performing blind IQA algorithms on our database and presented insights on how mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models in our paper.

Investigators

The investigators in this research are:

Copyright Notice

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Copyright (c) 2015 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:

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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