  Image Quality Assessment (IQA) with reference images have achieved great
success by imitating the human vision system, in which the image quality is
effectively assessed by comparing the query image with its pristine reference
image. However, for the images in the wild, it is quite difficult to access
accurate reference images. We argue that it is possible to learn reference
knowledge under the No-Reference Image Quality Assessment (NR-IQA) setting,
which is effective and efficient empirically. Concretely, by innovatively
introducing a novel feature distillation method in IQA, we propose a new
framework to learn comparative knowledge from non-aligned reference images. And
then, to achieve fast convergence and avoid overfitting, we further propose an
inductive bias regularization. Such a framework not only solves the congenital
defects of NR-IQA but also improves the feature extraction framework, enabling
it to express more abundant quality information. Surprisingly, our method
utilizes less input while obtaining a more significant improvement compared to
the teacher models. Extensive experiments on eight standard NR-IQA datasets
demonstrate the superior performance to the state-of-the-art NR-IQA methods,
i.e., achieving the PLCC values of 0.917 (vs. 0.884 in LIVEC) and 0.686 (vs.
0.661 in LIVEFB).
