Image quality assessing model by using neural network and support vector machine

Pear signal to noise ratio (PSNR) and structure similarity (SSIM) as two indexes describing image quality were used with neural network (NN) and support vector machine (SVM) to set up new effective image quality assessing model. The definition of isolated points and the prediction of isolated points were illuminated. NN was used to obtain the image quality assessing mapping functions and SVM was used to classify the samples into different types. UTexas image database was used in simulation experiment. With the same level of consistency of quality assessing model, the prediction monotonicity of the model is 7.42% higher than PSNR. The root mean square error (RMSE) of the model is 36.06% higher than PSNR. The number of isolated points with the new model was reduced and the performance of the model was enhanced. The results from simulation experiment show the model valid. The output of the new model can effectively reflect the image subjective quality.