Blind Image Quality Assessment With Joint Entropy Degradation

Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents...
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Gespeichert in:

Autor*in:	Weiping Ji [verfasserIn] Jinjian Wu [verfasserIn] Man Zhang [verfasserIn] Zuozhi Liu [verfasserIn] Guangming Shi [verfasserIn] Xuemei Xie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Blind image quality assessment (BIQA) local receptive field joint visual entropy joint probability distribution

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 30925-30936
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:30925-30936

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2901063

Katalog-ID:	DOAJ01532138X

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520			\|a Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.
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source	In IEEE Access 7(2019), Seite 30925-30936 volume:7 year:2019 pages:30925-30936
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topic_facet	Blind image quality assessment (BIQA) local receptive field joint visual entropy joint probability distribution Electrical engineering. Electronics. Nuclear engineering
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authorswithroles_txt_mv	Weiping Ji @@aut@@ Jinjian Wu @@aut@@ Man Zhang @@aut@@ Zuozhi Liu @@aut@@ Guangming Shi @@aut@@ Xuemei Xie @@aut@@
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callnumber-first	T - Technology
author	Weiping Ji
spellingShingle	Weiping Ji misc TK1-9971 misc Blind image quality assessment (BIQA) misc local receptive field misc joint visual entropy misc joint probability distribution misc Electrical engineering. Electronics. Nuclear engineering Blind Image Quality Assessment With Joint Entropy Degradation
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topic_title	TK1-9971 Blind Image Quality Assessment With Joint Entropy Degradation Blind image quality assessment (BIQA) local receptive field joint visual entropy joint probability distribution
topic	misc TK1-9971 misc Blind image quality assessment (BIQA) misc local receptive field misc joint visual entropy misc joint probability distribution misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Blind image quality assessment (BIQA) misc local receptive field misc joint visual entropy misc joint probability distribution misc Electrical engineering. Electronics. Nuclear engineering
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title	Blind Image Quality Assessment With Joint Entropy Degradation
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title_full	Blind Image Quality Assessment With Joint Entropy Degradation
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title_sort	blind image quality assessment with joint entropy degradation
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title_auth	Blind Image Quality Assessment With Joint Entropy Degradation
abstract	Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.
abstractGer	Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.
abstract_unstemmed	Blind image quality assessment (BIQA) aims to evaluate the quality of an image without pristine image objectively, which is highly desired in many perception-oriented image processing systems. Distortions degrade the visual contents and cause image quality degradation. Moreover, the visual contents of an image suffer from individual degradations by different types and different levels of distortions, which makes us difficult to analyze the quality degradation. From the perspective of information theory, there is a decrease in the amount of the visual contents when images are distorted. Therefore, in this paper, the image quality is assessed through its visual entropy degradation. Researches on the neuroscience indicate that the simple cells in the local receptive field can be characterized as being spatially localized and oriented, then the local intensity, gradient, and orientation features are extracted to represent the visual contents of an image. By deducing the joint entropy equation, the joint entropy is related to the statistical distributions. Next, in order to measure the visual entropy, the joint statistical distributions of those features are calculated. Finally, by measuring the degradations on these distributions of distorted images, a novel BIQA method is proposed. The experimental results on the databases of LIVE, CSIQ, and TID2013 show that the proposed method has superior performance than other state-of-the-art BIQA methods.
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title_short	Blind Image Quality Assessment With Joint Entropy Degradation
url	https://doi.org/10.1109/ACCESS.2019.2901063 https://doaj.org/article/6795a9e0bf9c417e8f7711c4f2acca70 https://ieeexplore.ieee.org/document/8666985/ https://doaj.org/toc/2169-3536
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