General-purpose no-reference image quality assessment approaches still lag the advances in full-reference methods. Most no-reference methods are either distortion specific (i.e. they quantify one or more distortions such as blur, blockiness, or ringing), or they train a learning machine based on a large number of features. In this approach, we propose a discrete cosine transform (DCT) statistics-based support vector machine (SVM) approach based on only 3 features in the DCT domain. The approach extracts a very small number of features and is entirely in the DCT domain, making it computationally convenient. The results are shown to correlate highly with human visual perception of quality.
Michele A. Saad, Alan C. Bovik, Christophe Charrie