融合熵特征与随机森林，统一评估自然图像和屏幕内容质量
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
- 用梯度图的排列熵提取图像结构复杂度特征
- 在21000+张图像上超越40+现有方法，跨类型表现更优
- 适合需要统一质量评估的多媒体压缩与流媒体应用
图像质量评估（IQA）在图像压缩、修复和多媒体流传输中至关重要。然而，现有指标在自然场景图像（NSIs）与屏幕内容图像（SCIs）间泛化能力差，因其结构与感知特性差异大。为此，提出新型全参考IQA框架：基于排列熵的特征融合随机森林（PEFRF）。该方法从参考图、失真图及融合图的梯度图中提取排列熵，构建鲁棒特征向量，并输入训练于主观评分的随机森林回归器，预测最终质量。在包含21,000余张图像的13个基准数据集及40+种先进IQA方法上验证，结果表明PEFRF在多种失真类型与内容域下均持续优于现有方法，证明其作为跨内容图像质量评估的统一且统计显著的解决方案的有效性。
原文摘要 · Abstract (English)
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.
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