Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance

• URL: http://arxiv.org/abs/2412.15847v1
• Date: Fri, 20 Dec 2024 12:39:49 GMT
• Title: Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance
• Authors: Xuekai Wei, Junyu Zhang, Qinlin Hu, Mingliang Zhou\\Yong Feng, Weizhi Xian, Huayan Pu, Sam Kwong,
• Abstract summary: Current full-reference image quality assessment (FR-IQA) methods often fuse features from reference and distorted images.<n>This work introduces a pioneering training-free FR-IQA method that accurately predicts image quality in alignment with the human visual system.
• Score: 47.01352278293561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.}

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