No-reference image quality assessment for contrast-changed images via a semi-supervised robust PCA model

Contrast plays an important role in human perception of image quality. In this paper, we propose a metric for no-reference quality assessment of contrast-changed images by using a novel semi-supervised robust PCA, which can realize feature selection and denoising simultaneously, guided by the available supervisory information. To select features adaptively, the information-oriented features (e.g. entropy and natural scene statistics) and appearance-oriented features (e.g. colorfulness) are adopted. The proposed model is formulated as a constraint optimization problem, which is further casted to a convex problem and solved via augmented Lagrangian multiplier method. Extensive experimental results on CCID2014, CSIQ, SIQAD and TID2013 databases show that the proposed semi-supervised image quality metric based on robust PCA (SIQMR) provides a more accurate prediction than other metrics on the human perception of contrast variations.