Blind Image Quality Assessment Based on Geometric Order Learning
Authors: Nyeong-Ho Shin, Seon-Ho Lee, Chang-Su Kim

A novel approach to blind image quality assessment called quality comparison network (QCN) is proposed in this paper which sorts the feature vectors of input images according to their quality scores in an embedding space. QCN employs comparison transformers (CTs) and score pivots which act as the centroids of feature vectors of similar-quality images. Each CT updates the score pivots and the feature vectors of input images based on their ordered correlation. To this end we adopt four loss functions. Then we estimate the quality score of a test image by searching the nearest score pivot to its feature vector in the embedding space. Extensive experiments show that the proposed QCN algorithm yields excellent image quality assessment performances on various datasets. Furthermore QCN achieves great performances in cross-dataset evaluation demonstrating its superb generalization capability. The source codes are available at https://github.com/nhshin-mcl/QCN.