  Objective assessment of image quality is fundamentally important in many
image processing tasks. In this work, we focus on learning blind image quality
assessment (BIQA) models which predict the quality of a digital image with no
access to its original pristine-quality counterpart as reference. One of the
biggest challenges in learning BIQA models is the conflict between the gigantic
image space (which is in the dimension of the number of image pixels) and the
extremely limited reliable ground truth data for training. Such data are
typically collected via subjective testing, which is cumbersome, slow, and
expensive. Here we first show that a vast amount of reliable training data in
the form of quality-discriminable image pairs (DIP) can be obtained
automatically at low cost by exploiting large-scale databases with diverse
image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no
subjective opinions are used for training) model using RankNet, a pairwise
learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a
perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index.
Extensive experiments on four benchmark IQA databases demonstrate that dipIQ
outperforms state-of-the-art OU-BIQA models. The robustness of dipIQ is also
significantly improved as confirmed by the group MAximum Differentiation (gMAD)
competition method. Furthermore, we extend the proposed framework by learning
models with ListNet (a listwise L2R algorithm) on quality-discriminable image
lists (DIL). The resulting DIL Inferred Quality (dilIQ) index achieves an
additional performance gain.
