Image quality assessment based on singular value decomposition in multiple feature domains

To solve the insufficient generalization ability of the traditional image quality assessment (IQA) algorithms, an image quality predication (IQP) model based on the singular value decomposition in multiple feature domains was proposed. The visual features were extracted by comparing the difference of singular values and singular vectors between the corresponding local neighborhoods of reference and test images in the multiple feature domains (images and their gradient and phase congruency maps), and then fed into a support vector machine (SVM) to predict the perceptual quality of images. Subsequent experiments show that, proposed IQP model built on the SVM not only has a better performance than the traditional IQA algorithms on individual image databases, but also exhibits good generalization ability by having a good across-image-database performance. By replacing the SVM with an ensemble learner, the generalization ability of the proposed IQP model can be improved further.