Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information
This paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important fo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Digital Multimedia Broadcasting |
| Online Access: | http://dx.doi.org/10.1155/2022/7540810 |
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| _version_ | 1832549599768739840 |
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| author | Hua-Wen Chang Xiao-Dong Bi Cheng-Yang Du Chang-Wei Mao Ming-Hui Wang |
| author_facet | Hua-Wen Chang Xiao-Dong Bi Cheng-Yang Du Chang-Wei Mao Ming-Hui Wang |
| author_sort | Hua-Wen Chang |
| collection | DOAJ |
| description | This paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important for IQE. Visual saliency can find the most attractive regions to the human visual system in a given image. These attractive image regions are more strongly correlated with image quality results. In addition, the degradation of gradient information is related to the structure distortion which is a very important factor for image quality. However, the two types of features cannot accurately evaluate the color distortion of images. In order to evaluate chromatic distortion, this paper proposes the color similarity which is measured in the YIQ color space. The computation of the proposed method begins with the similarity calculation of local gradient information, visual saliency, and color information. Then, the final quality score is obtained by the standard deviation on each similarity component. The experimental results on five benchmark databases (i.e., CSIQ, IVC, LIVE, TID2013, and TID2008) show that the proposed IQE method performs better than other methods in the correlation with subjective quality judgment. |
| format | Article |
| id | doaj-art-9f512ad4d2c544fe8df41aebeece3e77 |
| institution | Kabale University |
| issn | 1687-7586 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Digital Multimedia Broadcasting |
| spelling | doaj-art-9f512ad4d2c544fe8df41aebeece3e772025-02-03T06:10:55ZengWileyInternational Journal of Digital Multimedia Broadcasting1687-75862022-01-01202210.1155/2022/7540810Image Quality Evaluation Based on Gradient, Visual Saliency, and Color InformationHua-Wen Chang0Xiao-Dong Bi1Cheng-Yang Du2Chang-Wei Mao3Ming-Hui Wang4College of Computer and Communication EngineeringCollege of Computer and Communication EngineeringCollege of Computer and Communication EngineeringCollege of Computer and Communication EngineeringCollege of Computer ScienceThis paper proposes an image quality evaluation (IQE) metric by considering gradient, visual saliency, and color information. Visual saliency and gradient information are two types of effective features for quality evaluation research. Different regions within an image are not uniformly important for IQE. Visual saliency can find the most attractive regions to the human visual system in a given image. These attractive image regions are more strongly correlated with image quality results. In addition, the degradation of gradient information is related to the structure distortion which is a very important factor for image quality. However, the two types of features cannot accurately evaluate the color distortion of images. In order to evaluate chromatic distortion, this paper proposes the color similarity which is measured in the YIQ color space. The computation of the proposed method begins with the similarity calculation of local gradient information, visual saliency, and color information. Then, the final quality score is obtained by the standard deviation on each similarity component. The experimental results on five benchmark databases (i.e., CSIQ, IVC, LIVE, TID2013, and TID2008) show that the proposed IQE method performs better than other methods in the correlation with subjective quality judgment.http://dx.doi.org/10.1155/2022/7540810 |
| spellingShingle | Hua-Wen Chang Xiao-Dong Bi Cheng-Yang Du Chang-Wei Mao Ming-Hui Wang Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information International Journal of Digital Multimedia Broadcasting |
| title | Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information |
| title_full | Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information |
| title_fullStr | Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information |
| title_full_unstemmed | Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information |
| title_short | Image Quality Evaluation Based on Gradient, Visual Saliency, and Color Information |
| title_sort | image quality evaluation based on gradient visual saliency and color information |
| url | http://dx.doi.org/10.1155/2022/7540810 |
| work_keys_str_mv | AT huawenchang imagequalityevaluationbasedongradientvisualsaliencyandcolorinformation AT xiaodongbi imagequalityevaluationbasedongradientvisualsaliencyandcolorinformation AT chengyangdu imagequalityevaluationbasedongradientvisualsaliencyandcolorinformation AT changweimao imagequalityevaluationbasedongradientvisualsaliencyandcolorinformation AT minghuiwang imagequalityevaluationbasedongradientvisualsaliencyandcolorinformation |