Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment
Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/4752378 |
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| author | Ruizhe Deng Yang Zhao Yong Ding |
| author_facet | Ruizhe Deng Yang Zhao Yong Ding |
| author_sort | Ruizhe Deng |
| collection | DOAJ |
| description | Image quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases. |
| format | Article |
| id | doaj-art-c53f38ae4d4f43639b4775422ccc5a8e |
| institution | Kabale University |
| issn | 2314-4904 2314-4912 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj-art-c53f38ae4d4f43639b4775422ccc5a8e2025-02-03T01:29:59ZengWileyJournal of Engineering2314-49042314-49122017-01-01201710.1155/2017/47523784752378Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality AssessmentRuizhe Deng0Yang Zhao1Yong Ding2College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, ChinaCollege of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, ChinaImage quality assessment (IQA) is desired to evaluate the perceptual quality of an image in a manner consistent with subjective rating. Considering the characteristics of hierarchical visual cortex, a novel full reference IQA method is proposed in this paper. Quality-aware features that human visual system is sensitive to are extracted to describe image quality comprehensively. Concretely, log Gabor filters and local tetra patterns are employed to capture spatial frequency and local texture features, which are attractive to the primary and secondary visual cortex, respectively. Moreover, images are enhanced before feature extraction with the assistance of visual saliency maps since visual attention affects human evaluation of image quality. The similarities between the features extracted from distorted image and corresponding reference images are synthesized and mapped into an objective quality score by support vector regression. Experiments conducted on four public IQA databases show that the proposed method outperforms other state-of-the-art methods in terms of both accuracy and robustness; that is, it is highly consistent with subjective evaluation and is robust across different databases.http://dx.doi.org/10.1155/2017/4752378 |
| spellingShingle | Ruizhe Deng Yang Zhao Yong Ding Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment Journal of Engineering |
| title | Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment |
| title_full | Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment |
| title_fullStr | Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment |
| title_full_unstemmed | Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment |
| title_short | Hierarchical Feature Extraction Assisted with Visual Saliency for Image Quality Assessment |
| title_sort | hierarchical feature extraction assisted with visual saliency for image quality assessment |
| url | http://dx.doi.org/10.1155/2017/4752378 |
| work_keys_str_mv | AT ruizhedeng hierarchicalfeatureextractionassistedwithvisualsaliencyforimagequalityassessment AT yangzhao hierarchicalfeatureextractionassistedwithvisualsaliencyforimagequalityassessment AT yongding hierarchicalfeatureextractionassistedwithvisualsaliencyforimagequalityassessment |