Learning to Rank for Blind Image Quality Assessment

Understand

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images.

• State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model.
• However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations.
• To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as "the quality of image $I_a$ is better than that of image $I_b$" for training a robust BIQA model.