DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator

Deep neural networks pre-trained on ImageNet have demonstrated remarkable transferability for developing effective full-reference image quality assessment (FR-IQA) models. However, existing approaches typically demand pixel-level alignment between reference and distorted images—a requirement that poses significant challenges in practical scenarios involving natural photography and texture similarity evaluation. To address this limitation, we propose a novel FR-IQA model leveraging deep statistical similarity derived from pre-trained features without relying on spatial co-location of these features or requiring fine-tuning with mean opinion scores. Specifically, we employ distance correlation, a potent yet relatively underexplored statistical measure, to quantify similarity between reference and distorted images within a deep feature space. The distance correlation is computed via the ratio of the distance covariance to the product of their respective distance standard deviations, for which we derive a closed-form solution using the inner product of deep double-centered distance matrices. Extensive experimental evaluations across diverse IQA benchmarks demonstrate the superiority and robustness of the proposed model. Furthermore, we demonstrate the utility of our model for optimizing texture synthesis and neural style transfer tasks, achieving state-of-the-art performance in both quantitative measures and qualitative assessments.