标题： 一种统一图像质量评估的混合方法：基于排列熵的特征与随机森林融合，用于自然场景和屏幕内容图像的跨内容应用 显示英文标题

标题： 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

摘要： 图像质量评估（IQA）在图像压缩、修复和多媒体流等应用中起着至关重要的作用。 然而，由于自然场景图像（NSIs）和屏幕内容图像（SCIs）在结构和感知特性上的差异，现有的度量方法往往难以在多种图像类型之间进行泛化。 为解决这一限制，我们提出了一种新的全参考IQA框架：基于排列熵的特征与随机森林融合（PEFRF）。 PEFRF通过从参考图像、失真图像和融合图像的梯度图中提取排列熵来捕捉结构复杂性，形成一个鲁棒的特征向量。 然后将这些特征输入到一个在主观质量评分上训练的随机森林回归器中，以预测最终的图像质量。 该框架在包含超过21,000张图像和40多个最先进的IQA度量的13个基准数据集上进行了评估。 实验结果表明，PEFRF在各种失真类型和内容领域中始终优于现有方法，确立了其作为跨内容图像质量评估的统一且统计显著解决方案的有效性。 显示英文摘要

摘要： 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.