计算机工程2011，Vol.37Issue(10)：195-197,3.DOI:10.3969/j.issn.1000-3428.2011.10.067
基于SVM和GA的图像质量评价方法
Image Quality Assessment Method Based on Support Vector Machine and Genetic Algorithm
摘要
Abstract
Aiming at the limitation of correlation method of subjective assessment and its poor effect when facing the problems of local optimum,nonlinearity, higher dimension and small sample, the objective assessment indexes of image quality such as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR) and Singular Value Decomposition(SVD), and the scores from LIVE database are used as the learning sample. By using Support Vector Machine(SVM) to train a correlation function between subjective and objective and Genetic Algorithm(GA) for parameter optimization, a perfect model is obtained to achieve the consistency of subjective and objective. Experimental results show that compared with traditional methods, the assessment with the method is more accurate.关键词
支持向量机/遗传算法/图像质量评价/最优参数选取/主客观一致性Key words
Support Vector Machine(SVM)/ Genetic Algorithm(GA)/ image quality assessment/ optimal parameter selection/ consistency of subjective and objective分类
信息技术与安全科学引用本文复制引用
王磊,丁文锐,向锦武,崔乐..基于SVM和GA的图像质量评价方法[J].计算机工程,2011,37(10):195-197,3.基金项目
航空科学基金资助项目(2008ZC51029) （2008ZC51029）
