| Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique |
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| 作者姓名： | 王海 沈庭芝 谢志宏 |
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| 作者单位： | DepartmentofElectronicEngineering,SchoolofInformationScienceandTechnology,BeijingInstituteofTechnology,Beijing100081,China |
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| 基金项目： | theMinisterialLevelAdvancedResearchFoundation ( 2 0 0 2 0 960 0 0 1) |
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| 摘 要： | A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective.
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| 关 键 词： | 混合模糊推断 遗传算法 图象质量估计 图象恢复 |
| 收稿时间： | 2003/6/17 0:00:00 |
Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique |
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| WANG Hai,SHEN Ting-zhi and XIE Zhi-hong.Blind Image Quality Assessment Based on Hybrid Fuzzy-Genetic Technique[J].Journal of Beijing Institute of Technology,2003,12(4):395-398. |
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| Authors: | WANG Hai SHEN Ting-zhi and XIE Zhi-hong |
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| Affiliation: | Department of Electronic Engineering, School of Information Science and Technology, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | A new method for no-reference image quality assessment based on hybrid fuzzy-genetic technique is proposed. Noise variance and edge sharpness level of the restored image are two basic metrics for assessing the performance of the restoration algorithm, then a fuzzy if-then inference system is developed to combine the two metrics to get a final quality score, and the parameters of the fuzzy membership function are trained with genetic algorithms. Experiments results show that the image quality score correlates well with mean opinion score and the proposed approach is robust and effective. |
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| Keywords: | image quality assessment fuzzy inference system genetic algorithms |
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