Smartphone image quality assessment method based on Swin-AK Transformer

This paper proposes a smartphone image quality assessment method that combines the Swin-AK Transformer based on alterable kernel convolution and manual features based on dual attention cross-fusion. Firstly, manual features that affected image quality were extracted. These features could capture subtle visual changes in images. Secondly, the Swin-AK Transformer was presented and it could improve the extraction and processing of local information. In addition, a dual attention cross-fusion module was designed, integrating spatial attention and channel attention mechanisms to fuse manual features with deep features. Experimental results show that the Pearson correlation coefficients on the SPAQ and LIVE-C datasets reached 0.932 and 0.885, respectively, while the Spearman rank-order correlation coefficients reached 0.929 and 0.858, respectively. These results demonstrate that the proposed method in this paper can effectively predict the quality of smartphone images.