提出融合统计、多尺度与深度学习的图像质量评估新方法
Hybrid Image Resolution Quality Metric (HIRQM):A Comprehensive Perceptual Image Quality Assessment Framework
- 结合像素分布、多尺度结构与语义特征，全面评估图像质量
- 在TID2013和LIVE数据集上相关系数达0.92（皮尔逊）和0.90（斯皮尔曼）
- 动态加权机制提升对噪声、模糊等复杂失真的适应性，适合图像压缩与修复
传统图像质量评估指标如均方误差和结构相似性指数在复杂失真下难以反映感知质量。本文提出混合图像分辨率质量度量（HIRQM），整合统计、多尺度与基于深度学习的方法，实现全面的质量评估。HIRQM包含三个部分：局部像素分布分析的概率密度函数，跨分辨率结构完整性评估的多尺度特征相似性，以及使用预训练VGG16网络提取的分层深度图像特征，以匹配人类感知。通过亮度和方差等图像特性动态调整各组件权重，增强对不同失真类型的适应性。实验在TID2013和LIVE数据集上进行，结果表明，其皮尔逊相关系数达0.92，斯皮尔曼相关系数达0.90，优于传统方法。该模型在处理噪声、模糊和压缩伪影方面表现优异，适用于图像压缩与恢复等应用。
原文摘要 · Abstract (English)
Traditional image quality assessment metrics like Mean Squared Error and Structural Similarity Index often fail to reflect perceptual quality under complex distortions. We propose the Hybrid Image Resolution Quality Metric (HIRQM), integrating statistical, multi-scale, and deep learning-based methods for a comprehensive quality evaluation. HIRQM combines three components: Probability Density Function for local pixel distribution analysis, Multi-scale Feature Similarity for structural integrity across resolutions, and Hierarchical Deep Image Features using a pre-trained VGG16 network for semantic alignment with human perception. A dynamic weighting mechanism adapts component contributions based on image characteristics like brightness and variance, enhancing flexibility across distortion types. Our contributions include a unified metric and dynamic weighting for better perceptual alignment. Evaluated on TID2013 and LIVE datasets, HIRQM achieves Pearson and Spearman correlations of 0.92 and 0.90, outperforming traditional metrics. It excels in handling noise, blur, and compression artifacts, making it valuable for image processing applications like compression and restoration.
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