BSD: Blind image quality assessment based on structural degradation
Research in biological vision and neurology has evidenced that there are separate mechanisms in human visual cortex to process the first- and second-order patterns. Image structures detected by a linear filter are the first-order patterns which describe luminance changes, while patterns that are inv...
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| Published in: | Neurocomputing (Amsterdam) Vol. 236; pp. 93 - 103 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
02-05-2017
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| Abstract | Research in biological vision and neurology has evidenced that there are separate mechanisms in human visual cortex to process the first- and second-order patterns. Image structures detected by a linear filter are the first-order patterns which describe luminance changes, while patterns that are invisible to linear filters are often referred as the second-order structures. In this paper, we propose a general-purpose blind image quality assessment (BIQA) method by taking account of both the first- and second-order image structures. Specifically, the Prewitt linear filters are used to extract first-order image structures and the local contrast normalization is employed to extract second-order image structures. Perceptual features are extracted from these two image structural maps and used as the input to a support vector regression to model the nonlinear relationship between feature space to human opinion score. Extensive experiments on five image databases manifest the outstanding performance of the proposed method compared to the relevant state-of-the-art BIQA methods. |
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| AbstractList | Research in biological vision and neurology has evidenced that there are separate mechanisms in human visual cortex to process the first- and second-order patterns. Image structures detected by a linear filter are the first-order patterns which describe luminance changes, while patterns that are invisible to linear filters are often referred as the second-order structures. In this paper, we propose a general-purpose blind image quality assessment (BIQA) method by taking account of both the first- and second-order image structures. Specifically, the Prewitt linear filters are used to extract first-order image structures and the local contrast normalization is employed to extract second-order image structures. Perceptual features are extracted from these two image structural maps and used as the input to a support vector regression to model the nonlinear relationship between feature space to human opinion score. Extensive experiments on five image databases manifest the outstanding performance of the proposed method compared to the relevant state-of-the-art BIQA methods. |
| Author | Fang, Yuming Lin, Weisi Li, Qiaohong |
| Author_xml | – sequence: 1 givenname: Qiaohong surname: Li fullname: Li, Qiaohong organization: School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore – sequence: 2 givenname: Weisi surname: Lin fullname: Lin, Weisi email: wslin@ntu.edu.sg organization: School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore – sequence: 3 givenname: Yuming surname: Fang fullname: Fang, Yuming organization: School of Information Technology, Jiangxi University of Finance and Economics, 330013 Nanchang, China |
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| Keywords | Gradient Image quality assessment (IQA) Human visual system (HVS) Texture No-reference (NR) |
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