Dynamic Neuropsychological Approach for Multi-Quality Image Assessment Using Grey-Topological Data Analysis

With the development of the brain-computer interface, it is possible to assess the image quality based on an electroencephalogram (EEG) signal, which is essential to construct a quality assessment system that accords with the characteristics of the human visual system. However, as the complexity of image degradation levels rises, the analysis of brain features becomes progressively more challenging, leading the neural mechanisms still unclear. In this paper, an image quality assessment experiment with three levels of degradation to explore the neural mechanisms of subjects when confronted with images degraded at multiple levels. Subsequently, we proposed a dynamic model combined with Topological Data Analysis (TDA) and Grey Theory to extract the feature of brain response to different distortion-level images, which is called grey-topological data analysis (Grey-TDA) in this paper. The results indicate that the proposed method is effective in identifying brain responses under stimuli of varying image qualities, which contributes to the research of image quality assessment from the perspective of brain cognition.