Pairwise Learning to Rank for Image Quality Assessment

Because the pairwise comparison is a natural and effective way to obtain subjective image quality scores, we propose an objective full-reference image quality assessment (FR-IQA) index based on pairwise learning to rank (PLR). We first compose a large number of pairs of images, extract their features, and compute their preference labels as training labels. We then obtain a pairwise preference model by training a binary classifier using the features and labels. Because image quality is affected by the masking effect, we propose extracting frequency-aware quality features by adapting state-of-the-art IQA metrics. The learned pairwise preference model is then used to predict the preference between pairs of images in the testing dataset. The quality of each image is computed as the number of preferences. Experimental results on four IQA databases validate that the proposed PLR-based IQA index achieves higher consistency with human subjective evaluation than the state-of-the-art IQA metrics.