An image quality assessment algorithm based on 'global + local' feature fusion

Recently, there has been increasing research on image quality assessment. Among the existing mainstream approaches, image feature extraction tends to be simplistic, leading to insufficient quality information extraction and underutilization of the extracted data. Additionally, the correlation between different regions of the image is often neglected. This study proposes an image quality assessment algorithm based on global-local feature fusion (IQA-GL). First, the global and local features of the image are extracted separately, and irrelevant information in the local features is filtered out. Then, a global-local feature fusion model is constructed to enhance the interaction of feature information and gather image quality data across all feature channels. Finally, the relationship between individual image patches and the global image is modeled, adjusting the weights of each image patch to aggregate a quality score for the global image. Experimental results show the IQA-GL performs excellently on public datasets. This study innovatively combines global and local features, offering a new perspective for image quality assessment.