NDLI: Improved SSIM IQA of contrast distortion based on the contrast sensitivity characteristics of HVS

Content Provider	IET Digital Library
Author	Yao, Juncai Liu, Guizhong
Abstract	Currently, the structural similarity index metric (SSIM) is recognised generally and applied widely in image quality assessment (IQA). However, using SSIM to evaluate contrast-distorted images from TID2013 and CSIQ databases is low effective. In this study, the authors improve SSIM for contrast-distorted images by combining it with the contrast sensitivity characteristics of human visual system (HVS). In the improved method, first, they combine the visual characteristics to propose a model that HVS perceives the real image. Then, this model is used to eliminate the visual redundancy of real images. Afterwards, the perceived images are evaluated using SSIM. Furthermore, 241 contrast-distorted images from TID2013 and CSIQ databases were used in experiments. The results have shown that in comparison with SSIM scores, the scores obtained by the improved SSIM are more consistent with the subjective assessment scores. Moreover, the Pearson linear correlation coefficient and Spearman rank order correlation coefficient between subjective and objective scores are averagely improved by 12.83 and 22.78%, respectively. In addition, the assessment accuracy of the improved SSIM is better than that of five commonly used IQA metrics. Also, it has an excellent generalisation performance. These results show that the assessment performance of the improved SSIM is effectively enhanced.
Starting Page	872
Ending Page	879
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0209
Journal	IET Image Processing
Publisher Date	2018-01-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Contrast-distorted Image CSIQ Databases Human Visual System HVS Contrast Sensitivity Characteristic Image Processing Objective Image Quality Assessment Optical, Image And Video Signal Processing Pearson Linear Correlation Coefficient Real Image Visual Redundancy Elimination Redundancy Reliability Spearman Rank Order Correlation Coefficient SSIM IQA Structural Similarity Index Metric TID2013 Database
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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