基于BP神经网络的图像质量评价参数优化  

Optimization of image quality assessment parameters based on back-propagation neural network

作　　者：范媛媛[1,2,3] 桑英军[1] 沈湘衡[2] 

机构地区：[1]淮阴工学院,江苏淮安223003 [2]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [3]中国科学院研究生院,北京100039

出　　处：《应用光学》2011年第6期1150-1155,共6页Journal of Applied Optics

基　　金：中国科学院创新基金资助项目(YZ200904)

摘　　要：在基于噪声图像的无参考峰值信噪比质量评价方法中,为了得到最优的阈值参数,提出以图像块均方误差阈值threshold1、噪声检测阈值threshold2为输入因子,以Pearson相关系数和Spearman等级相关系数为输出因子,以实验值为样本建立[2 7 2]单隐层BP神经网络模型,应用BP神经网络的泛化能力实现对相关阈值参数的预测优化,为阈值参数的选择提供理论依据。实验结果表明,所建立的数学模型可靠,预测结果与试验值的偏差小,训练好的BP神经网络能够比较准确地预测不同阈值参数下的相关系数。优化后,选取threshold1=101,threshold2=4,Pearson相关系数达到了-0.895 0,Spearman等级相关系数达到了-0.913 6,评价效果得到提高,且节省大量时间。In no reference peak signal to noise ratio（PSNR） image quality assessment based on noisy images,in order to get optimal threshold parameters,it is proposed that taking experiment values as a sample,a back-propagation（BP） neural network model is established with the mean square error（MSE） threshold1 of image block and the noise detection threshold2 as the input factors,and the Person and Spearman correlation coefficients as the output factors.The model realizes the prediction of relevant parameters by its generalization capability and offers a theoretical foundation for parameters selection.Experiments indicate that the model is reliable.The prediction results show little difference from the experimental data.The trained BP neural network can precisely predict the relevant parameters.After optimizing,threshold1=101 and threshold2=4 are selected,Pearson Correlation Coefficient and Spearman Rank Order Correlation Coefficient reaches-0.895 0 and-0.913 6 respectively.The assessment result improves a lot,and much time is saved.

关 键 词：图像质量评价 参数优化 BP神经网络 预测 泛化 

分 类 号：TN206[电子电信—物理电子学] TP391.4[自动化与计算机技术—计算机应用技术]