  Measuring the perceptual quality of images automatically is an essential task
in the area of computer vision, as degradations on image quality can exist in
many processes from image acquisition, transmission to enhancing. Many Image
Quality Assessment(IQA) algorithms have been designed to tackle this problem.
However, it still remains un settled due to the various types of image
distortions and the lack of large-scale human-rated datasets. In this paper, we
propose a novel algorithm based on the Swin Transformer [31] with fused
features from multiple stages, which aggregates information from both local and
global features to better predict the quality. To address the issues of
small-scale datasets, relative rankings of images have been taken into account
together with regression loss to simultaneously optimize the model.
Furthermore, effective data augmentation strategies are also used to improve
the performance. In comparisons with previous works, experiments are carried
out on two standard IQA datasets and a challenge dataset. The results
demonstrate the effectiveness of our work. The proposed method outperforms
other methods on standard datasets and ranks 2nd in the no-reference track of
NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that
our method is promising in solving diverse IQA problems and thus can be used to
real-word applications.
