A no‐reference blurred colourful image quality assessment method based on dual maximum local information

Abstract Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No‐reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing‐based a...

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Autores principales:	Jian Chen, Shiyun Li, Li Lin
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/99bd0b5e6f444e649c95acd51c97d116
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id	oai:doaj.org-article:99bd0b5e6f444e649c95acd51c97d116
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spelling	oai:doaj.org-article:99bd0b5e6f444e649c95acd51c97d1162021-11-09T10:16:47ZA no‐reference blurred colourful image quality assessment method based on dual maximum local information1751-96831751-967510.1049/sil2.12064https://doaj.org/article/99bd0b5e6f444e649c95acd51c97d1162021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12064https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No‐reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing‐based auto‐focussing and maximum local information theories, a no‐reference blurred colourful IQA method based on Dual Maximum Local Information is proposed here. First, a window extraction method that combines the maximum gradient with local entropy is proposed to obtain the region of interest (ROI) for subsequent processing. Second, an improved maximum gradient method that leverages information from different channel images is presented to calculate the maximum gradient variation within the ROI for final sharpness score. Experimental results illustrated that the proposed method has better performance under various measurements compared with the state‐of‐the‐art methods on LIVE, CSIQ, TID2008, TID2013, VCL@FER, IVC image databases.Jian ChenShiyun LiLi LinWileyarticleTelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 597-611 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Jian Chen Shiyun Li Li Lin A no‐reference blurred colourful image quality assessment method based on dual maximum local information
description	Abstract Images can be blurred due to the imperfection of the imaging system and blurriness is one of the challenging problems for image quality assessment (IQA). No‐reference blurred IQA methods have been proposed in the literature to calculate image blurriness. Inspired by image processing‐based auto‐focussing and maximum local information theories, a no‐reference blurred colourful IQA method based on Dual Maximum Local Information is proposed here. First, a window extraction method that combines the maximum gradient with local entropy is proposed to obtain the region of interest (ROI) for subsequent processing. Second, an improved maximum gradient method that leverages information from different channel images is presented to calculate the maximum gradient variation within the ROI for final sharpness score. Experimental results illustrated that the proposed method has better performance under various measurements compared with the state‐of‐the‐art methods on LIVE, CSIQ, TID2008, TID2013, VCL@FER, IVC image databases.
format	article
author	Jian Chen Shiyun Li Li Lin
author_facet	Jian Chen Shiyun Li Li Lin
author_sort	Jian Chen
title	A no‐reference blurred colourful image quality assessment method based on dual maximum local information
title_short	A no‐reference blurred colourful image quality assessment method based on dual maximum local information
title_full	A no‐reference blurred colourful image quality assessment method based on dual maximum local information
title_fullStr	A no‐reference blurred colourful image quality assessment method based on dual maximum local information
title_full_unstemmed	A no‐reference blurred colourful image quality assessment method based on dual maximum local information
title_sort	no‐reference blurred colourful image quality assessment method based on dual maximum local information
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/99bd0b5e6f444e649c95acd51c97d116
work_keys_str_mv	AT jianchen anoreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation AT shiyunli anoreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation AT lilin anoreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation AT jianchen noreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation AT shiyunli noreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation AT lilin noreferenceblurredcolourfulimagequalityassessmentmethodbasedondualmaximumlocalinformation
_version_	1718441130872274944

A no‐reference blurred colourful image quality assessment method based on dual maximum local information

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