Image Quality Assessment: Unifying Local and Global Distortions via Fisher Information Geometry and Optimal Sinkhorn Transport

A prevailing limitation of existing full-reference image quality assessment (FR-IQA) methods is their segregated focus on either local texture or global structure, preventing a unified analysis of these two critical and interrelated dimensions of human perception. To resolve this, we propose the Fisher Sinkhorn image quality assessment (FSIQA) framework, which establishes a principled unification of information geometry and optimal transport theory. FSIQA leverages a dual-branch architecture: its Fisher branch captures localized, nonGaussian texture distortions by modelling higher-order statistics on a Riemannian manifold, whereas the complementary Sinkhorn branch assesses global structural deformations via entropy-regularized optimal transport. This synergistic framework creates a mathematical continuum for local-to-global degradation analysis and is notably independent of the backbone network architecture, thus requiring no dataset-specific retraining. Comprehensive experiments across five benchmark datasets validate the efficacy of our approach, demonstrating that FSIQA consistently outperforms existing methods and establishes a new state-of-the-art method in the field.