No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain

Ismail T. Ahmed, Soong Der Chen

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

1 Citation (Scopus)

Abstract

Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and No-Reference Quality metric for Contrast-Distorted Images (NR-IQACDI) are the state-of-the-art IQA for Contrast-Distorted Images (CDI). Nevertheless, there is room for improvement especially for the assessment results using image database called TID2013 and CSIQ. Most of the existing No-Reference Image Quality Assessment Algorithm (NR-IQA) metrics designed for CDI use features in spatial domain. In the current work, we pursue to compliment it with feature in Curvelet domain which is powerful in capturing multiscale and multidirectional information in an image. Indeed, the Directional Contrast (DC) is captured in the Curvelet domain of CDI by decomposing the image into several directional subbands across multiple scales using curvelet transform. Due to the fact that high-frequency subband consists of many directional information, the directional contrast of each directional subband coefficient is generated as feature vector. Finally a Support Vector Regressor (SVR) is used to predict the image quality score. Experiments are conducted to assess the effect of adding DC feature in the Curvelet domain. The experimental results based on i-fold cross validation with K ranging from 2 to 10 and statistical test indicate that the performance of NRIQACDI can be improved by adding DC feature in the Curvelet domain.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781509011841
DOIs
Publication statusPublished - 10 Oct 2017
Event13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017 - Penang, Malaysia
Duration: 10 Mar 201712 Mar 2017

Other

Other13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017
CountryMalaysia
CityPenang
Period10/03/1712/03/17

Fingerprint

image contrast
Image quality
Statistical tests
statistical tests
Experiments
coefficients

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Instrumentation

Cite this

Ahmed, I. T., & Chen, S. D. (2017). No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017 (pp. 61-66). [8064925] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSPA.2017.8064925
Ahmed, Ismail T. ; Chen, Soong Der. / No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain. Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 61-66
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Ahmed, IT & Chen, SD 2017, No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain. in Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017., 8064925, Institute of Electrical and Electronics Engineers Inc., pp. 61-66, 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017, Penang, Malaysia, 10/03/17. https://doi.org/10.1109/CSPA.2017.8064925

No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain. / Ahmed, Ismail T.; Chen, Soong Der.

Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 61-66 8064925.

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

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Ahmed IT, Chen SD. No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 61-66. 8064925 https://doi.org/10.1109/CSPA.2017.8064925