论文标题
基于频率差异的高动态范围图像质量评估
High Dynamic Range Image Quality Assessment Based on Frequency Disparity
论文作者
论文摘要
在本文中，提出了一种基于高动态范围（HDR）图像的频率差异的新颖有效的图像质量评估（IQA）算法，称为基于局部全球频率特征模型（LGFM）。由假设人类视觉系统高度适应于在感知视觉场景时提取结构信息和部分频率的假设，Gabor和Butterworth滤镜分别用于HDR图像的亮度来提取本地和全球频率特征。相似性测量和特征池在频率特征上依次执行，以获得预测的质量评分。在四个广泛使用的基准上评估的实验表明，与最先进的HDR IQA方法相比，所提出的LGFM可以提供更高的主观感知一致性。我们的代码可在：\ url {https://github.com/eezkni/lgfm}中获得。
In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that the human visual system is highly adapted for extracting structural information and partial frequencies when perceiving the visual scene, the Gabor and the Butterworth filters are applied to the luminance of the HDR image to extract local and global frequency features, respectively. The similarity measurement and feature pooling are sequentially performed on the frequency features to obtain the predicted quality score. The experiments evaluated on four widely used benchmarks demonstrate that the proposed LGFM can provide a higher consistency with the subjective perception compared with the state-of-the-art HDR IQA methods. Our code is available at: \url{https://github.com/eezkni/LGFM}.
