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 ［2 7 2］ 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.