WAQNIQA: Wavelet-Augmented Quaternion Network for No-Reference Image Quality Assessment
提出WAQNIQA网络，结合小波变换和四元数特征增强，解决无参考图像质量评估中CNN局部偏差和Transformer高计算复杂度问题，在电力场景和公共数据集上超越现有方法。
No-reference image quality assessment (NR-IQA) plays a pivotal role in computer vision by enabling image quality evaluation without reference images. While recent CNN and Transformer-based methods have advanced feature extraction, they face significant limitations. CNNs exhibit local feature bias, limiting their ability to capture global dependencies and complex structures critical to understanding diverse distortions. Transformers, despite modeling nonlocal dependencies through multihead attention, suffer from quadratic computational complexity with spatial dimensions, hindering efficient multiscale analysis. Moreover, their attention mechanisms frequently overlook critical interchannel dependencies, which are vital for capturing fine details in texture-rich images. Coupled with difficulties in handling high-noise environments and complex textures, this results in limited real-world accuracy and poor generalization across diverse datasets and unknown distortions. To bridge these gaps, we propose WAQNIQA, a novel wavelet-augmented quaternion network for NR-IQA. Distinct from conventional architectures, WAQNIQA integrates two synergistic modules: the wavelet-infused adaptive attention (WIAA) module, which leverages wavelet transforms (WTs) to achieve robust multiscale spatial-frequency analysis with linear complexity, and the quaternion collaborative feature enhancement (QCFE) module, which holistically models interchannel correlations to preserve fine texture details. Furthermore, we introduce PowerGridIQ, the first NR-IQA dataset specifically tailored for power grid scenarios. Extensive experiments demonstrate that WAQNIQA consistently surpasses state-of-the-art CNN and Transformer-based methods on PowerGridIQ and six public benchmarks. Notably, WAQNIQA exhibits superior cross-domain generalization, achieving competitive performance on the AGIQA-1K dataset for AI-generated content (AIGC) without explicit semantic alignment training, thereby validating its robustness against diverse and unknown distortions. Our code is available at <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <uri>https://github.com/king-huoye/WAQNIQA</uri> </monospace>