Current full-reference image quality assessment (FR-IQA) methods often fuse features from reference and distorted images, overlooking that color and luminance distortions occur mainly at low frequencies, whereas edge and texture distortions occur at high frequencies. This work introduces a pioneering training-free FR-IQA method that accurately predicts image quality in alignment with the human visual system (HVS) by leveraging a novel perceptual degradation modelling approach to address this limitation. First, a collaborative feature refinement module employs a carefully designed wavelet transform to extract perceptually relevant features, capturing multiscale perceptual information and mimicking how the HVS analyses visual information at various scales and orientations in the spatial and frequency domains. Second, a Hausdorff distance-based distribution similarity measurement module robustly assesses the discrepancy between the feature distributions of the reference and distorted images, effectively handling outliers and variations while mimicking the ability of HVS to perceive and tolerate certain levels of distortion. The proposed method accurately captures perceptual quality differences without requiring training data or subjective quality scores. Extensive experiments on multiple benchmark datasets demonstrate superior performance compared with existing state-of-the-art approaches, highlighting its ability to correlate strongly with the HVS.\footnote{The code is available at \url{https://anonymous.4open.science/r/CVPR2025-F339}.}

翻译：当前的全参考图像质量评估方法通常直接融合参考图像与失真图像的特征，忽略了色彩与亮度失真主要发生在低频分量，而边缘与纹理失真则主要存在于高频分量。本研究提出了一种开创性的免训练全参考图像质量评估方法，通过采用新颖的感知退化建模策略来克服这一局限，从而能够准确预测符合人类视觉系统感知的图像质量。首先，协同特征精炼模块采用精心设计的小波变换来提取感知相关特征，捕获多尺度感知信息，模拟人类视觉系统在空间域与频域中多尺度、多方向分析视觉信息的方式。其次，基于Hausdorff距离的分布相似性度量模块能够稳健地评估参考图像与失真图像特征分布之间的差异，有效处理异常值与分布变化，同时模拟人类视觉系统对特定程度失真的感知与容忍能力。所提方法无需训练数据或主观质量分数即可准确捕捉感知质量差异。在多个基准数据集上的大量实验表明，该方法相比现有先进方法具有更优越的性能，凸显了其与人类视觉系统高度相关的特性。\footnote{代码发布于 \url{https://anonymous.4open.science/r/CVPR2025-F339}。}