  Automatic perception of image quality is a challenging problem that impacts
billions of Internet and social media users daily. To advance research in this
field, we propose a no-reference image quality assessment (NR-IQA) method
termed Cross-IQA based on vision transformer(ViT) model. The proposed Cross-IQA
method can learn image quality features from unlabeled image data. We construct
the pretext task of synthesized image reconstruction to unsupervised extract
the image quality information based ViT block. The pretrained encoder of
Cross-IQA is used to fine-tune a linear regression model for score prediction.
Experimental results show that Cross-IQA can achieve state-of-the-art
performance in assessing the low-frequency degradation information (e.g., color
change, blurring, etc.) of images compared with the classical full-reference
IQA and NR-IQA under the same datasets.
