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

TL;DR

This paper introduces PEFRF, a novel full-reference image quality assessment method that combines permutation entropy features with a random forest model, effectively handling diverse natural and screen-content images for cross-content applications.

Contribution

The paper presents a new IQA framework that fuses permutation entropy features with random forest regression, improving cross-content image quality prediction over existing methods.

Findings

PEFRF outperforms 40+ state-of-the-art IQA metrics across 13 benchmark datasets.

It effectively generalizes across natural-scene and screen-content images.

The method demonstrates statistically significant improvements in quality prediction accuracy.

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.