A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications

Abstract

Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differing structural and perceptual characteristics. To address this limitation, we propose a novel full-reference IQA framework: Permutation Entropy-based Features Fused with Random Forest (PEFRF). PEFRF captures structural complexity by extracting permutation entropy from the gradient maps of reference, distorted, and fused images, forming a robust feature vector. These features are then input into a Random Forest regressor trained on subjective quality scores to predict final image quality. The framework is evaluated on 13 benchmark datasets comprising over 21,000 images and 40+ state-of-the-art IQA metrics. Experimental results demonstrate that PEFRF consistently outperforms existing methods across various distortion types and content domains, establishing its effectiveness as a unified and statistically significant solution for cross-content image quality assessment.