Visual State Space Model With Graph-Based Feature Aggregation for No-Reference Image Quality Assessment

Inspired by the human visual system (HVS), no-reference image quality assessment (NR-IQA) has made significant progress without relying on perfect reference images. The HVS is primarily influenced by the combined effects of representational information with different receptive fields and attribute categories when capturing subjective perceived quality. However, existing methods only roughly or partially utilize representations of multi-dimensional information. Furthermore, current NR-IQA methods either rely on convolutional neural networks (CNNs) with limited local perception or depend on the computational complexity of vision transformers (ViTs). To make up for the shortcomings of these two architectures, an emerging visual state space model (VMamba) is introduced. Motivated by this, this paper presents a NR-IQA method via VIsual State space model with Graph-based feature Aggregation (VISGA). Specifically, we utilize a plain, pre-training-free, and feature-enhanced VMamba as the backbone. To align with the perceptual mechanisms of the HVS by effectively using features with different dimensional information, a graph convolutional network-based multi-receptive field and multi-level aggregation module is designed to deeply explore the correlations and interactions of multi-dimensional representations. Additionally, we propose a gated local enhancement module with patch-wise perception to enhance the local perception of VMamba. Extensive experiments conducted on seven databases demonstrate that VISGA achieves outstanding performance. Notably, our model remains state-of-the-art when training with very few parameters. The code is released at https://github.com/xirihao/VISGA.