Blind Image Quality Assessment Using Visual Neuron Model and Visual Attention Mechanism

In this paper, a blind image quality assessment (BIQA) method is proposed, which leverages both a visual neuron model and visual attention mechanism. The research of BIQA is aimed at evaluating the perceptual quality of images without access to reference images. In order to make the quality scores of the BIQA methods more in line with the visual perception, modeling the human visual system (HVS) is an effective approach. The neurons in the visual cortex are the key components of the HVS, and their response characteristics can be described by scene statistics models. Therefore, we design a visual neuron model to simulate cortical responses. This model is implemented using kernel principal component analysis (KPCA) to simulate the complexity of neural responses, thereby enabling the extraction of perceptually relevant statistical features. The attention mechanism of the HVS allows it to focus on the most salient regions of an image. The visual attention network (VAN), utilizing large‐kernel convolutions, effectively captures long‐range dependencies and global information within an image, making it suitable for extracting attention features. The extracted statistical and attention features are then fused and input into a regression network to predict the image quality score. Experimental results on multiple benchmark datasets demonstrate that the proposed method outperforms state‐of‐the‐art IQA models.