  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. The
preference label, representing the relative quality of two images, is generally
precise and consistent, and is not sensitive to image content, distortion type,
or subject identity; such PIPs can be generated at very low cost. The proposed
BIQA method is one of learning to rank. We first formulate the problem of
learning the mapping from the image features to the preference label as one of
classification. In particular, we investigate the utilization of a multiple
kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A
simple but effective strategy to estimate perceptual image quality scores is
then presented. Experiments show that the proposed BIQA method is highly
effective and achieves comparable performance to state-of-the-art BIQA
algorithms. Moreover, the proposed method can be easily extended to new
distortion categories.
