From Pixels to Rich-Nodes: A Cognition-Inspired Framework for Blind Image Quality Assessment

Blind image quality assessment (BIQA) is a subjective perception-driven task, which necessitates assessment results consistent with human cognition. The human cognitive system inherently involves both separation and integration mechanisms. Recent works have witnessed the success of deep learning methods in separating distortion features. Nonetheless, traditional deep-learning-based BIQA methods predominantly depend on fixed topology to mimic the information integration in the brain, which gives rise to scale sensitivity and low flexibility. To handle this challenge, we delve into the dynamic interactions among neurons and propose a cognition-inspired BIQA model. Drawing insights from the rich club structure in network neuroscience, a graph-inspired feature integrator is devised to reconstruct the network topology. Specifically, we argue that the activity of individual neurons (pixels) tends to exhibit a random fluctuation with ambiguous meaning, while clear and coherent cognition arises from neurons with high connectivity (rich-nodes). Therefore, a self-attention mechanism is employed to establish strong semantic associations between pixels and rich-nodes. Subsequently, we design intra- and inter-layer graph structures to promote the feature interaction across spatial and scale dimensions. Such dynamic circuits endow the BIQA method with efficient, flexible, and robust information processing capabilities, so as to achieve more human-subjective assessment results. Moreover, since the limited samples in existing IQA datasets are prone to model overfitting, we devise two prior hypotheses: frequency prior and ranking prior. The former stepwise augments high-frequency components that reflect the distortion degree during the multilevel feature extraction, while the latter seeks to motivate the model’s in-depth comprehension of differences in sample quality. Extensive experiments on five publicly datasets reveal that the proposed algorithm achieves competitive results.