Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics
Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not acc...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Xiaohong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 79(2020), 35-36 vom: 08. Juli, Seite 25905-25920 |
|---|---|
| Übergeordnetes Werk: |
volume:79 ; year:2020 ; number:35-36 ; day:08 ; month:07 ; pages:25905-25920 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-020-09222-9 |
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OLC211905522X |
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| 520 | |a Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. | ||
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10.1007/s11042-020-09222-9 doi (DE-627)OLC211905522X (DE-He213)s11042-020-09222-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Xiaohong verfasserin aut Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. IQA Real distorted K-means High-level semantics Pang, Yunjie (orcid)0000-0002-3412-2327 aut Ma, Xiangcai aut Enthalten in Multimedia tools and applications Springer US, 1995 79(2020), 35-36 vom: 08. Juli, Seite 25905-25920 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:79 year:2020 number:35-36 day:08 month:07 pages:25905-25920 https://doi.org/10.1007/s11042-020-09222-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 79 2020 35-36 08 07 25905-25920 |
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10.1007/s11042-020-09222-9 doi (DE-627)OLC211905522X (DE-He213)s11042-020-09222-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Xiaohong verfasserin aut Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. IQA Real distorted K-means High-level semantics Pang, Yunjie (orcid)0000-0002-3412-2327 aut Ma, Xiangcai aut Enthalten in Multimedia tools and applications Springer US, 1995 79(2020), 35-36 vom: 08. Juli, Seite 25905-25920 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:79 year:2020 number:35-36 day:08 month:07 pages:25905-25920 https://doi.org/10.1007/s11042-020-09222-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 79 2020 35-36 08 07 25905-25920 |
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10.1007/s11042-020-09222-9 doi (DE-627)OLC211905522X (DE-He213)s11042-020-09222-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Xiaohong verfasserin aut Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. IQA Real distorted K-means High-level semantics Pang, Yunjie (orcid)0000-0002-3412-2327 aut Ma, Xiangcai aut Enthalten in Multimedia tools and applications Springer US, 1995 79(2020), 35-36 vom: 08. Juli, Seite 25905-25920 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:79 year:2020 number:35-36 day:08 month:07 pages:25905-25920 https://doi.org/10.1007/s11042-020-09222-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 79 2020 35-36 08 07 25905-25920 |
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10.1007/s11042-020-09222-9 doi (DE-627)OLC211905522X (DE-He213)s11042-020-09222-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Wang, Xiaohong verfasserin aut Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. IQA Real distorted K-means High-level semantics Pang, Yunjie (orcid)0000-0002-3412-2327 aut Ma, Xiangcai aut Enthalten in Multimedia tools and applications Springer US, 1995 79(2020), 35-36 vom: 08. Juli, Seite 25905-25920 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:79 year:2020 number:35-36 day:08 month:07 pages:25905-25920 https://doi.org/10.1007/s11042-020-09222-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 79 2020 35-36 08 07 25905-25920 |
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real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics |
| title_auth |
Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics |
| abstract |
Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstractGer |
Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
| abstract_unstemmed |
Abstract Most of the existing image quality assessment (IQA) methods are directed to artificially synthesized distorted images, in which the types and characteristics of distortion are different from those in the real world. In view of the fact that the existing non-reference IQA methods can not accurately evaluate the quality of the real distortion image, combined with the theoretical analysis of multi-layer visual perception mechanism, we propose a real image distortion IQA method based on image underlying features and high-level semantics. Considering non-linear hierarchical structure of human visual perception, firstly, k-means clustering algorithm is performed according to the underlying feature indexs of the image so that the used image database can be divided into several groups, which aims to improve the accuracy of predicted quality score. Secondly, the deep convolutional neural network (DCNN) is used to extract the first-grade high-level semantic features in each group. Then, second-grade high-level semantic features that can provide better representation of image features are obtained by performing multiple statistical functions on first-grade high-level semantics. Besides, we establish an effective high-capacity regressor with high-level semantics and subjective mean opinion scores (MOS) values of the human eyes. The experimental results show that the proposed model on the KonIQ-10 k image database can predict the quality score effectively and achieve a high consistency with the corresponding MOS value, which is helpful for the subsequent image enhancement. © Springer Science+Business Media, LLC, part of Springer Nature 2020 |
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| title_short |
Real distorted images quality assessment based on multi-layer visual perception mechanism and high-level semantics |
| url |
https://doi.org/10.1007/s11042-020-09222-9 |
| remote_bool |
false |
| author2 |
Pang, Yunjie Ma, Xiangcai |
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Pang, Yunjie Ma, Xiangcai |
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| doi_str |
10.1007/s11042-020-09222-9 |
| up_date |
2024-07-03T23:01:59.616Z |
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