Image Quality Assessment: Exploring Joint Degradation Effect of Deep Network Features Via Kernel Representation Similarity Analysis

Typically, deep network-based full-reference image quality assessment (FR-IQA) models compare deep features from reference and distorted images pairwise, overlooking correlations among features from the same source. We propose a dual-branch framework to capture the joint degradation effect among deep network features. The first branch uses kernel representation similarity analysis (KRSA), which compares feature self-similarity matrices via the mean absolute error (MAE). The second branch conducts pairwise comparisons via the MAE, and a training-free logarithmic summation of both branches derives the final score. Our approach contributes in three ways. First, integrating the KRSA with pairwise comparisons enhances the model's perceptual awareness. Second, our approach is adaptable to diverse network architectures. Third, our approach can guide perceptual image enhancement. Extensive experiments on 10 datasets validate our method's efficacy, demonstrating that perceptual deformation widely exists in diverse IQA scenarios and that measuring the joint degradation effect can discern appealing content deformations. The codes are available at https://github.com/Buka-Xing/Dual-Branch-Image-Quality-Assessment.