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

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.