ARQUE: A Hybrid Multi-Expert Framework for No-Reference Image Quality Assessment Using Curvature Analysis

Abstract

Quantitative image quality assessment (IQA) is a central task in computer vision. This work presents a unified framework, based on directional curvature analysis, that can be parameterized to operate in multiple functional modes. It was demonstrated that the framework can be optimized to act as a state-of-the-art no-reference quality metric (NR-IQA) for two distinct distortion classes. Gaussian blur (p = 0.933) and white noise (p = -0.948), in correlation with human perception in the LIVE dataset. The main contribution is the formulation of a hybrid system that uses the response signature of two optimized filters to first diagnose the artifact type (blur vs. noise) with over 97% accuracy and, then, quantify the magnitude of the degradation using an expert regression model. The final hybrid system achieved predictive performance with an RMSE of 5.29 DMOS points on the combined dataset, establishing it as a robust tool for image quality diagnosis.