Blind image quality assessment by pairwise ranking image series

Image quality assessment (IQA) is constantly innovating, but there are still three types of stickers that have not been resolved: the "content sticker" — limitation of training set, the "annotation sticker" — subjective instability in opinion scores and the "distortion sticker" — disordered distortion settings. In this paper, a No-Reference Image Quality Assessment (NR IQA) approach is proposed to deal with the problems. For "content sticker", we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network; For "annotation sticker", the absolute noise-containing subjective scores are transformed into ranking comparison results, and we design an indirect unsupervised regression based on Eigenvalue Decomposition (EVD); For "distortion sticker", we propose a perception-based distortion classification method, which makes the distortion types clear and refined. Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability. Furthermore, the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.