No-reference image quality assessment using gradient magnitude and wiener filtered wavelet features

No-reference image quality assessment (NR-IQA) aims to evaluate the perceived quality of distorted images without prior knowledge of pristine version of the images. The quality score is predicted based on the features extracted from the distorted image, which needs to correlate with the mean opinion score. The prediction of an image quality score becomes a trivial task, if the noise affecting the quality of an image can be modeled. In this paper, gradient magnitude and Wiener filtered discrete wavelet coefficients are utilized for image quality assessment. In order to reconstruct an estimated noise image, Wiener filter is applied to discrete wavelet coefficients. The estimated noise image and the gradient magnitude are modeled as conditional Gaussian random variables. Joint adaptive normalization is applied to the conditional random distribution of the estimated noise image and the gradient magnitude to form a feature vector. The feature vector is used as an input to a pre-trained support vector regression model to predict the image quality score. The proposed NR-IQA is tested on five commonly used image quality assessment databases and shows better performance as compared to the existing NR-IQA techniques. The experimental results show that the proposed technique is robust and has good generalization ability. Moreover, it also shows good performance when training is performed on images from one database and testing is performed on images from another database.