Enhancement of no-reference image quality assessment for contrast-distorted images using natural scene statistics features in Curvelet domain

Ismail Taha Ahmed, Soong Der Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Contrast is a very important characteristic for visual perception of image quality. Some No-Reference Image Quality Assessment Algorithm NR-IQA metrics for Contrast-Distorted Images (CDI) have been proposed in the literature, e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQACDI). Here, we intend to improve the assessment results of images available in databases such as TID2013 and CSIQ. Most of the NR-IQA metrics (e.g. NR-IQACDI) designed for CDI adopt features available in the spatial domain. This paper proposes to compliment it with feature in Curvelet domain which is powerful in capturing multiscale and multidirectional information in an image. We employed the Natural Scene Statistics (NSS) features in Curvelet domain originally recommended by Liu et al. (2014) which were found useful in the assessment of the quality of image distorted by compression, noise and blurring. Experiments were then conducted to assess the effect of incorporating these NSS features. The experimental results based on K-fold cross validation (K ranged from 2 to 10) and statistical test showed that the performance of NRIQACDI was improved. Future works include improvements of NRIQACDI, exploration of feature fusion methods and using a suitable feature selection method.

Original languageEnglish
Title of host publication2017 7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-133
Number of pages6
ISBN (Electronic)9781538603833
DOIs
Publication statusPublished - 27 Nov 2017
Event7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Shah Alam, Malaysia
Duration: 02 Oct 201703 Oct 2017

Publication series

Name2017 7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Proceedings

Other

Other7th IEEE International Conference on System Engineering and Technology, ICSET 2017
CountryMalaysia
CityShah Alam
Period02/10/1703/10/17

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All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Ahmed, I. T., & Chen, S. D. (2017). Enhancement of no-reference image quality assessment for contrast-distorted images using natural scene statistics features in Curvelet domain. In 2017 7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Proceedings (pp. 128-133). [8123433] (2017 7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSEngT.2017.8123433