Blind multiply distorted image quality assessment using relevant perceptual features

We propose a new learning quality-aware features (LQAF) blind image quality assessment (IQA) algorithm for multiply distorted images. In the new model, some relevant quality perceptual features, including mean value of intensity, contrast sensitivity function (CSF), mean value of gradient magnitude, and 15 texture parameters of gray level-gradient co-occurrence matrix (GGCM) from four categories images: original distorted image and its phase congruency (PC) image, covariance maximum and minimum image of phase congruency, are used. Image quality estimation is accomplished by the approximating function between these features and subjective mean opinion scores using support vector regression (SVR). Experimental results on the LIVE multiply distorted image database (LIVEMD) demonstrate the effectiveness of our proposed method.