NDLI: Beyond Human Opinion Scores: Blind Image Quality Assessment Based on Synthetic Scores

Content Provider	IEEE Xplore Digital Library
Author	Peng Ye Kumar, J. Doermann, D.
Copyright Year	2014
Description	Author affiliation: Inst. for Adv. Comput. Studies, Univ. of Maryland, College Park, MD, USA (Peng Ye; Doermann, D.) \|\| Xerox Res. Center, Webster, MA, USA (Kumar, J.)
Abstract	State-of-the-art general purpose Blind Image Quality Assessment (BIQA) models rely on examples of distorted images and corresponding human opinion scores to learn a regression function that maps image features to a quality score. These types of models are considered "opinion-aware" (OA) BIQA models. A large set of human scored training examples is usually required to train a reliable OA-BIQA model. However, obtaining human opinion scores through subjective testing is often expensive and time-consuming. It is therefore desirable to develop "opinion-free" (OF) BIQA models that do not require human opinion scores for training. This paper proposes BLISS (Blind Learning of Image Quality using Synthetic Scores). BLISS is a simple, yet effective method for extending OA-BIQA models to OF-BIQA models. Instead of training on human opinion scores, we propose to train BIQA models on synthetic scores derived from Full-Reference (FR) IQA measures. State-of-the-art FR measures yield high correlation with human opinion scores and can serve as approximations to human opinion scores. Unsupervised rank aggregation is applied to combine different FR measures to generate a synthetic score, which serves as a better "gold standard". Extensive experiments on standard IQA datasets show that BLISS significantly outperforms previous OF-BIQA methods and is comparable to state-of-the-art OA-BIQA methods.
Starting Page	4241
Ending Page	4248
File Size	377860
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.540
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Distortion measurement Training Transform coding Computational modeling PSNR Correlation Visualization unsupervised learning image quality
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition 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

Learning without Human Scores for Blind Image Quality Assessment

Learning a blind measure of perceptual image quality

Sparse representation for blind image quality assessment

Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

Unsupervised feature learning framework for no-reference image quality assessment

Image quality assessment using Histograms of Oriented Gradients

Evaluation of two principal approaches to objective image quality assessment

Blind Image Quality Assessment Using Semi-supervised Rectifier Networks

Statistical Modeling of Image Degradation Based on Quality Metrics

Beyond Human Opinion Scores: Blind Image Quality Assessment Based on Synthetic Scores

Similar Documents

Learning without Human Scores for Blind Image Quality Assessment

Learning a blind measure of perceptual image quality

Sparse representation for blind image quality assessment

Unsupervised Learning of Dictionaries of Hierarchical Compositional Models

Unsupervised feature learning framework for no-reference image quality assessment

Image quality assessment using Histograms of Oriented Gradients

Evaluation of two principal approaches to objective image quality assessment

Blind Image Quality Assessment Using Semi-supervised Rectifier Networks

Statistical Modeling of Image Degradation Based on Quality Metrics

Beyond Human Opinion Scores: Blind Image Quality Assessment Based on Synthetic Scores