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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Qiaohong [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:236 ; year:2017 ; day:2 ; month:05 ; pages:93-103 ; extent:11 |
| Links: |
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| DOI / URN: |
10.1016/j.neucom.2016.09.105 |
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| Katalog-ID: |
ELV030576822 |
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| 520 | |a 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. | ||
| 520 | |a 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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10.1016/j.neucom.2016.09.105 doi GBV00000000000095A.pica (DE-627)ELV030576822 (ELSEVIER)S0925-2312(16)31390-X DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Qiaohong verfasserin aut BSD: Blind image quality assessment based on structural degradation 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Gradient Elsevier Human visual system (HVS) Elsevier No-reference (NR) Elsevier Image quality assessment (IQA) Elsevier Texture Elsevier Lin, Weisi oth Fang, Yuming oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 https://doi.org/10.1016/j.neucom.2016.09.105 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 236 2017 2 0502 93-103 11 045F 610 |
| spelling |
10.1016/j.neucom.2016.09.105 doi GBV00000000000095A.pica (DE-627)ELV030576822 (ELSEVIER)S0925-2312(16)31390-X DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Qiaohong verfasserin aut BSD: Blind image quality assessment based on structural degradation 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Gradient Elsevier Human visual system (HVS) Elsevier No-reference (NR) Elsevier Image quality assessment (IQA) Elsevier Texture Elsevier Lin, Weisi oth Fang, Yuming oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 https://doi.org/10.1016/j.neucom.2016.09.105 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 236 2017 2 0502 93-103 11 045F 610 |
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10.1016/j.neucom.2016.09.105 doi GBV00000000000095A.pica (DE-627)ELV030576822 (ELSEVIER)S0925-2312(16)31390-X DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Qiaohong verfasserin aut BSD: Blind image quality assessment based on structural degradation 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Gradient Elsevier Human visual system (HVS) Elsevier No-reference (NR) Elsevier Image quality assessment (IQA) Elsevier Texture Elsevier Lin, Weisi oth Fang, Yuming oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 https://doi.org/10.1016/j.neucom.2016.09.105 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 236 2017 2 0502 93-103 11 045F 610 |
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10.1016/j.neucom.2016.09.105 doi GBV00000000000095A.pica (DE-627)ELV030576822 (ELSEVIER)S0925-2312(16)31390-X DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Qiaohong verfasserin aut BSD: Blind image quality assessment based on structural degradation 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Gradient Elsevier Human visual system (HVS) Elsevier No-reference (NR) Elsevier Image quality assessment (IQA) Elsevier Texture Elsevier Lin, Weisi oth Fang, Yuming oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 https://doi.org/10.1016/j.neucom.2016.09.105 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 236 2017 2 0502 93-103 11 045F 610 |
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10.1016/j.neucom.2016.09.105 doi GBV00000000000095A.pica (DE-627)ELV030576822 (ELSEVIER)S0925-2312(16)31390-X DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Li, Qiaohong verfasserin aut BSD: Blind image quality assessment based on structural degradation 2017transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Gradient Elsevier Human visual system (HVS) Elsevier No-reference (NR) Elsevier Image quality assessment (IQA) Elsevier Texture Elsevier Lin, Weisi oth Fang, Yuming oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 https://doi.org/10.1016/j.neucom.2016.09.105 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 236 2017 2 0502 93-103 11 045F 610 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:236 year:2017 day:2 month:05 pages:93-103 extent:11 |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Li, Qiaohong @@aut@@ Lin, Weisi @@oth@@ Fang, Yuming @@oth@@ |
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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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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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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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