Blind image quality assessment based on transformer

Abstract

Transformer has achieved milestones in natural language processing (NLP). Due to its excellent global and remote semantic information interaction performance, it has gradually been applied in vision tasks. In this paper, we propose PTIQ, which is a pure Transformer structure for Image Quality Assessment. Specifically, we use Swin Transformer Blocks as backbone to extract image features. The extracted feature vectors after extra state embedding and position embedding are fed into the original transformer encoder. Then, the output is passed to the MLP head to predict quality score. Experimental results demonstrate that the proposed architecture achieves outstanding performance.