  Image quality assessment (IQA) forms a natural and often straightforward
undertaking for humans, yet effective automation of the task remains highly
challenging. Recent metrics from the deep learning community commonly compare
image pairs during training to improve upon traditional metrics such as PSNR or
SSIM. However, current comparisons ignore the fact that image content affects
quality assessment as comparisons only occur between images of similar content.
This restricts the diversity and number of image pairs that the model is
exposed to during training. In this paper, we strive to enrich these
comparisons with content diversity. Firstly, we relax comparison constraints,
and compare pairs of images with differing content. This increases the variety
of available comparisons. Secondly, we introduce listwise comparisons to
provide a holistic view to the model. By including differentiable regularizers,
derived from correlation coefficients, models can better adjust predicted
scores relative to one another. Evaluation on multiple benchmarks, covering a
wide range of distortions and image content, shows the effectiveness of our
learning scheme for training image quality assessment models.
