  Although many effective models and real-world datasets have been presented
for blind image quality assessment (BIQA), recent BIQA models usually tend to
fit specific training set. Hence, it is still difficult to accurately and
robustly measure the visual quality of an arbitrary real-world image. In this
paper, a robust BIQA method, is designed based on three aspects, i.e., robust
training strategy, large-scale real-world dataset, and powerful backbone.
First, many individual models based on popular and state-of-the-art (SOTA)
Swin-Transformer (SwinT) are trained on different real-world BIQA datasets
respectively. Then, these biased SwinT-based models are jointly used to
generate pseudo-labels, which adopts the probability of relative quality of two
random images instead of fixed quality score. A large-scale real-world image
dataset with 1,000,000 image pairs and pseudo-labels is then proposed for
training the final cross-dataset-robust model. Experimental results on
cross-dataset tests show that the performance of the proposed method is even
better than some SOTA methods that are directly trained on these datasets, thus
verifying the robustness and generalization of our method.
