Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model. (arXiv:2209.10451v1 [cs.CV])

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 …

arxiv datasets image mixed quality