A human perception based performance evaluation of image quality metrics

Rameez Wajid, Atif Bin Mansoor, Marius Pedersen

Research output: Chapter in Book/Conference paperConference paperpeer-review

7 Citations (Scopus)


Though numerous image quality measures have been proposed, the search for a reliable IQM is still vigorously pursued by different research groups around the world. There is a need to compare the already proposed IQMs with respect to their adherence to human image quality perception. A model that can accurately simulate the human perception of image quality is a challenging task due to limited human knowledge in the related domains of psychology, vision, biology etc. The psycho-visual experiments remain the most accurate way to model human perception of visual quality. In this paper, different state of the art full-reference objective image quality metrics (IQMs) are evaluated against human subjective judgments on standard LIVE image quality database. The difference mean opinion scores (DMOS) were calculated from 17400 human judgments on 348 images distorted with white noise, Gaussian blur and Rayleigh fast-fading distortions. Subsequently, 13 leading IQMs like SSIM, VIF, FSIM, etc. were compared with DMOS on the basis of Pearson correlation coefficient. It is observed that though there is not a single winner, VIF and IFC seem to have a higher performance compared to other quality metrics.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings
EditorsJason Jerald, George Bebis, Bahram Parvin, Zhigang Deng, Richard Boyle, El Choubassi Maha, Hui Zhang, Darko Koracin, Ryan McMahan, Steven M. Drucker, Mark Carlson, Kambhamettu Chandra
Place of PublicationUSA
PublisherSpringer-Verlag Berlin
Number of pages10
ISBN (Electronic)9783319142487
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States
Duration: 8 Dec 201410 Dec 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Symposium on Visual Computing, ISVC 2014
Country/TerritoryUnited States
CityLas Vegas


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