FR-IQA: Joint Perceptual Degradation in Collaborative Feature Refinement

Abstract

A full-reference image quality assessment (FR-IQA) is proposed in this work. First, images are transformed into wavelet subbands and color histograms to maintain key visual characteristics of the human visual system (HVS). Second, subtle degradations are amplified via collaborative refinement. Afterward, the Hausdorff distance is used to capture local geometric discrepancies, rendering the measurement robust to severe artifacts and distortions. The experimental results demonstrate that the proposed approach accurately predicts subjective quality scores. Aligning more closely with HVS, the proposed method outperforms existing state-of-the-art methods on multiple benchmark databases. The code is available at https://github.com/OC007/IQACODE2025.