本文提出了一种基于双交叉注意力融合的Swin-AK Transformer(Swin Transformer based on alterable kernel convolution)和手工特征相结合的智能手机拍摄图像质量评价方法.首先,提取了影响图像质量的手工特征,这些特征可以捕捉到图像中细微的视觉变化;其次,提出了Swin-AK Transformer,增强了模型对局部信息的提取和处理能力.此外,本文设计了双交叉注意力融合模块,结合空间注意力和通道注意力机制,融合了手工特征与深度特征,实现了更加精确的图像质量预测.实验结果表明,在SPAQ和LIVE-C数据集上,皮尔森线性相关系数分别达到0.932和0.885,斯皮尔曼等级排序相关系数分别达到0.929和0.858.上述结果证明了本文提出的方法能够有效地预测智能手机拍摄图像的质量.
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.
NETL
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