No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain
In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Qiaohong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE signal processing letters - Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5, New York, NY, 19XX, 23(2016), 4, Seite 541-545 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:4 ; pages:541-545 |
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| DOI / URN: |
10.1109/LSP.2016.2537321 |
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| 520 | |a In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. | ||
| 650 | 4 | |a Quality assessment | |
| 650 | 4 | |a structural distortion | |
| 650 | 4 | |a Distortion | |
| 650 | 4 | |a no-reference (NR) | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a Feature extraction | |
| 650 | 4 | |a Image quality | |
| 650 | 4 | |a local binary pattern (LBP) | |
| 650 | 4 | |a Databases | |
| 650 | 4 | |a multiple distortions | |
| 650 | 4 | |a human visual system (HVS) | |
| 650 | 4 | |a Image quality assessment (IQA) | |
| 650 | 4 | |a Histograms | |
| 700 | 1 | |a Lin, Weisi |4 oth | |
| 700 | 1 | |a Fang, Yuming |4 oth | |
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10.1109/LSP.2016.2537321 doi PQ20160430 (DE-627)OLC1974211207 (DE-599)GBVOLC1974211207 (PRQ)ieee_primary_0b00006484fc27da0 (KEY)02390256u20160000023000400541noreferencequalityassessmentformultiplydistortedim DE-627 ger DE-627 rakwb eng 53.00 bkl Li, Qiaohong verfasserin aut No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. Quality assessment structural distortion Distortion no-reference (NR) Visualization Feature extraction Image quality local binary pattern (LBP) Databases multiple distortions human visual system (HVS) Image quality assessment (IQA) Histograms Lin, Weisi oth Fang, Yuming oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 4, Seite 541-545 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:4 pages:541-545 http://dx.doi.org/10.1109/LSP.2016.2537321 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 4 541-545 |
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10.1109/LSP.2016.2537321 doi PQ20160430 (DE-627)OLC1974211207 (DE-599)GBVOLC1974211207 (PRQ)ieee_primary_0b00006484fc27da0 (KEY)02390256u20160000023000400541noreferencequalityassessmentformultiplydistortedim DE-627 ger DE-627 rakwb eng 53.00 bkl Li, Qiaohong verfasserin aut No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. Quality assessment structural distortion Distortion no-reference (NR) Visualization Feature extraction Image quality local binary pattern (LBP) Databases multiple distortions human visual system (HVS) Image quality assessment (IQA) Histograms Lin, Weisi oth Fang, Yuming oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 4, Seite 541-545 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:4 pages:541-545 http://dx.doi.org/10.1109/LSP.2016.2537321 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 4 541-545 |
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10.1109/LSP.2016.2537321 doi PQ20160430 (DE-627)OLC1974211207 (DE-599)GBVOLC1974211207 (PRQ)ieee_primary_0b00006484fc27da0 (KEY)02390256u20160000023000400541noreferencequalityassessmentformultiplydistortedim DE-627 ger DE-627 rakwb eng 53.00 bkl Li, Qiaohong verfasserin aut No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. Quality assessment structural distortion Distortion no-reference (NR) Visualization Feature extraction Image quality local binary pattern (LBP) Databases multiple distortions human visual system (HVS) Image quality assessment (IQA) Histograms Lin, Weisi oth Fang, Yuming oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 4, Seite 541-545 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:4 pages:541-545 http://dx.doi.org/10.1109/LSP.2016.2537321 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 4 541-545 |
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10.1109/LSP.2016.2537321 doi PQ20160430 (DE-627)OLC1974211207 (DE-599)GBVOLC1974211207 (PRQ)ieee_primary_0b00006484fc27da0 (KEY)02390256u20160000023000400541noreferencequalityassessmentformultiplydistortedim DE-627 ger DE-627 rakwb eng 53.00 bkl Li, Qiaohong verfasserin aut No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. Quality assessment structural distortion Distortion no-reference (NR) Visualization Feature extraction Image quality local binary pattern (LBP) Databases multiple distortions human visual system (HVS) Image quality assessment (IQA) Histograms Lin, Weisi oth Fang, Yuming oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 4, Seite 541-545 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:4 pages:541-545 http://dx.doi.org/10.1109/LSP.2016.2537321 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 4 541-545 |
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10.1109/LSP.2016.2537321 doi PQ20160430 (DE-627)OLC1974211207 (DE-599)GBVOLC1974211207 (PRQ)ieee_primary_0b00006484fc27da0 (KEY)02390256u20160000023000400541noreferencequalityassessmentformultiplydistortedim DE-627 ger DE-627 rakwb eng 53.00 bkl Li, Qiaohong verfasserin aut No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. Quality assessment structural distortion Distortion no-reference (NR) Visualization Feature extraction Image quality local binary pattern (LBP) Databases multiple distortions human visual system (HVS) Image quality assessment (IQA) Histograms Lin, Weisi oth Fang, Yuming oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 4, Seite 541-545 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:4 pages:541-545 http://dx.doi.org/10.1109/LSP.2016.2537321 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 4 541-545 |
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10.1109/LSP.2016.2537321 |
| title_sort |
no-reference quality assessment for multiply-distorted images in gradient domain |
| title_auth |
No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain |
| abstract |
In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. |
| abstractGer |
In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. |
| abstract_unstemmed |
In practice, images available to consumers usually undergo several stages of processing including acquisition, compression, transmission, and presentation, and each stage may introduce certain type of distortion. It is common that images are simultaneously distorted by multiple types of distortions. Most existing objective image quality assessment (IQA) methods have been designed to estimate perceived quality of images corrupted by a single image processing stage. In this letter, we propose a no-reference (NR) IQA method to predict the visual quality of multiply-distorted images based on structural degradation. In the proposed method, a novel structural feature is extracted as the gradient-weighted histogram of local binary pattern (LBP) calculated on the gradient map (GWH-GLBP), which is effective to describe the complex degradation pattern introduced by multiple distortions. Extensive experiments conducted on two public multiply-distorted image databases have demonstrated that the proposed GWH-GLBP metric compares favorably with existing full-reference and NR IQA methods in terms of high accordance with human subjective ratings. |
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| title_short |
No-Reference Quality Assessment for Multiply-Distorted Images in Gradient Domain |
| url |
http://dx.doi.org/10.1109/LSP.2016.2537321 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7423683 |
| remote_bool |
false |
| author2 |
Lin, Weisi Fang, Yuming |
| author2Str |
Lin, Weisi Fang, Yuming |
| ppnlink |
182273075 |
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| isOA_txt |
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| author2_role |
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| doi_str |
10.1109/LSP.2016.2537321 |
| up_date |
2024-07-04T04:00:24.949Z |
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7.3985195 |