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

Fu-Zhao OU, Yuan-Gen WANG*, Guopu ZHU

*Corresponding author for this work

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

63 Citations (Scopus)

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
Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing
Subtitle of host publicationPROCEEDINGS
PublisherIEEE
Pages1004-1008
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event26th IEEE International Conference on Image Processing (ICIP 2019) - Taipei International Convention Center (TICC), Taipei, Taiwan
Duration: 22 Sept 201925 Sept 2019

Publication series

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

Conference

Conference26th IEEE International Conference on Image Processing (ICIP 2019)
Abbreviated titleIEEE ICIP 2019
Country/TerritoryTaiwan
CityTaipei
Period22/09/1925/09/19

Research Keywords

  • Image quality assessment
  • natural scene statistics
  • image distortion

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