General-purpose image quality assessment based on distortion-aware decision fusion

General-purpose image quality metrics aiming for quality prediction across various distortion types exhibit, on the whole, very limited effectiveness. In this paper, we propose a two-stage scheme to alleviate this limitation. At the first stage, probabilistic knowledge about the image distortion types is obtained based on a support-vector classification method. At the second stage, decision fusion of three existing image quality metrics is performed using the k-nearest-neighbor (k-NN) regression where the aforementioned probabilistic knowledge is utilized under an adaptive weighting scheme. We evaluate our method on the TID2008 database that is the largest publicly available image quality database containing 17 distortion types. The results strongly support the effectiveness and robustness of our method.