  Deep learning based image quality assessment (IQA) models usually learn to
predict image quality from a single dataset, leading the model to overfit
specific scenes. To account for this, mixed datasets training can be an
effective way to enhance the generalization capability of the model. However,
it is nontrivial to combine different IQA datasets, as their quality evaluation
criteria, score ranges, view conditions, as well as subjects are usually not
shared during the image quality annotation. In this paper, instead of aligning
the annotations, we propose a monotonic neural network for IQA model learning
with different datasets combined. In particular, our model consists of a
dataset-shared quality regressor and several dataset-specific quality
transformers. The quality regressor aims to obtain the perceptual qualities of
each dataset while each quality transformer maps the perceptual qualities to
the corresponding dataset annotations with their monotonicity maintained. The
experimental results verify the effectiveness of the proposed learning strategy
and our code is available at https://github.com/fzp0424/MonotonicIQA.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing