A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing
Subtitle of host publicationPROCEEDINGS
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages1004-1008
ISBN (electronic)978-1-5386-6249-6
ISBN (print)978-1-5386-6250-2
Publication statusPublished - 2019
Externally publishedYes

Publication series

Name
ISSN (Print)1522-4880
ISSN (electronic)2381-8549

Conference

Title26th IEEE International Conference on Image Processing (ICIP 2019)
LocationTaipei International Convention Center (TICC)
PlaceTaiwan
CityTaipei
Period22 - 25 September 2019

Abstract

Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods. The source code is available at https://github.com/GZU-Image-Video-Lab/NBIQA. © 2019 IEEE

Research Area(s)

  • Image quality assessment, natural scene statistics, image distortion

Citation Format(s)

A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics. / OU, Fu-Zhao; WANG, Yuan-Gen; ZHU, Guopu.
2019 IEEE International Conference on Image Processing: PROCEEDINGS. Institute of Electrical and Electronics Engineers, Inc., 2019. p. 1004-1008.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review