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

Image enhancement; Image retrieval; Signal processing; Curvelets; Directional contrasts; Distorted images; No-reference image quality assessments; Support vector regressor; Image quality

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Bibliographic Details
Main Authors: Ahmed I.T., Der C.S.
Other Authors: 57193324906
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-230972023-05-29T14:37:44Z No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain Ahmed I.T. Der C.S. 57193324906 7410253413 Image enhancement; Image retrieval; Signal processing; Curvelets; Directional contrasts; Distorted images; No-reference image quality assessments; Support vector regressor; Image quality 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. � 2017 IEEE. Final 2023-05-29T06:37:44Z 2023-05-29T06:37:44Z 2017 Conference Paper 10.1109/CSPA.2017.8064925 2-s2.0-85034808923 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034808923&doi=10.1109%2fCSPA.2017.8064925&partnerID=40&md5=0adbfd6655e1f40697e91c4b93a66cc9 https://irepository.uniten.edu.my/handle/123456789/23097 8064925 61 66 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Image enhancement; Image retrieval; Signal processing; Curvelets; Directional contrasts; Distorted images; No-reference image quality assessments; Support vector regressor; Image quality
author2 57193324906
author_facet 57193324906
Ahmed I.T.
Der C.S.
format Conference Paper
author Ahmed I.T.
Der C.S.
spellingShingle Ahmed I.T.
Der C.S.
No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
author_sort Ahmed I.T.
title No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
title_short No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
title_full No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
title_fullStr No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
title_full_unstemmed No-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
title_sort no-reference image quality assessment algorithm for contrast-distorted images enhanced by using directional contrast feature in curvelet domain
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806425573711413248
score 13.214268

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