NDLI: Blind Image Quality Assessment via Deep Learning

Content Provider	IEEE Xplore Digital Library
Author	Weilong Hou Xinbo Gao Dacheng Tao Xuelong Li
Copyright Year	2012
Abstract	This paper investigates how to blindly evaluate the visual quality of an image by learning rules from linguistic descriptions. Extensive psychological evidence shows that humans prefer to conduct evaluations qualitatively rather than numerically. The qualitative evaluations are then converted into the numerical scores to fairly benchmark objective image quality assessment (IQA) metrics. Recently, lots of learning-based IQA models are proposed by analyzing the mapping from the images to numerical ratings. However, the learnt mapping can hardly be accurate enough because some information has been lost in such an irreversible conversion from the linguistic descriptions to numerical scores. In this paper, we propose a blind IQA model, which learns qualitative evaluations directly and outputs numerical scores for general utilization and fair comparison. Images are represented by natural scene statistics features. A discriminative deep model is trained to classify the features into five grades, corresponding to five explicit mental concepts, i.e., excellent, good, fair, poor, and bad. A newly designed quality pooling is then applied to convert the qualitative labels into scores. The classification framework is not only much more natural than the regression-based models, but also robust to the small sample size problem. Thorough experiments are conducted on popular databases to verify the model's effectiveness, efficiency, and robustness.
Page Count	12
File Size	5810280
Starting Page	1275
Ending Page	1286
File Format	PDF
ISSN	2162237X
Volume Number	26
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image quality Databases Numerical models Training Visualization Image representation Measurement no reference Deep learning image quality assessment (IQA) natural scene statistics (NSS)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

Sl.	Authority	Responsibilities	Communication Details
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning

Learning to Rank for Blind Image Quality Assessment

No-reference quality assessment using natural scene statistics: JPEG2000

Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

No-Reference Image Quality Assessment in the Spatial Domain

No-Reference Image Sharpness Assessment in Autoregressive Parameter Space

Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images

Training Quality-Aware Filters for No-Reference Image Quality Assessment

Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

Blind Image Quality Assessment via Deep Learning

Similar Documents

Universal Blind Image Quality Assessment Metrics Via Natural Scene Statistics and Multiple Kernel Learning

Learning to Rank for Blind Image Quality Assessment

No-reference quality assessment using natural scene statistics: JPEG2000

Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

No-Reference Image Quality Assessment in the Spatial Domain

No-Reference Image Sharpness Assessment in Autoregressive Parameter Space

Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images

Training Quality-Aware Filters for No-Reference Image Quality Assessment

Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

Blind Image Quality Assessment via Deep Learning