  No-reference image quality assessment (NR-IQA) has received increasing
attention in the IQA community since reference image is not always available.
Real-world images generally suffer from various types of distortion.
Unfortunately, existing NR-IQA methods do not work with all types of
distortion. It is a challenging task to develop universal NR-IQA that has the
ability of evaluating all types of distorted images. In this paper, we propose
a universal NR-IQA method based on controllable list-wise ranking (CLRIQA).
First, to extend the authentically distorted image dataset, we present an
imaging-heuristic approach, in which the over-underexposure is formulated as an
inverse of Weber-Fechner law, and fusion strategy and probabilistic compression
are adopted, to generate the degraded real-world images. These degraded images
are label-free yet associated with quality ranking information. We then design
a controllable list-wise ranking function by limiting rank range and
introducing an adaptive margin to tune rank interval. Finally, the extended
dataset and controllable list-wise ranking function are used to pre-train a
CNN. Moreover, in order to obtain an accurate prediction model, we take
advantage of the original dataset to further fine-tune the pre-trained network.
Experiments evaluated on four benchmark datasets (i.e. LIVE, CSIQ, TID2013, and
LIVE-C) show that the proposed CLRIQA improves the state of the art by over 9%
in terms of overall performance. The code and model are publicly available at
https://github.com/GZHU-Image-Lab/CLRIQA.
