Lakhal Samy, Darmon Alexandre, Mastromatteo Iacopo, Marsili Matteo, Benzaquen Michael
Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
LadHyX, UMR CNRS 7646, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
Sci Rep. 2023 Sep 9;13(1):14879. doi: 10.1038/s41598-023-41714-0.
We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness [Formula: see text] and other relevant parameters. We then extend the analysis to natural images and find striking similarities with critical ([Formula: see text]) random textures. We show that the MSR is more robust and informative of image content than classical methods such as power spectrum analysis. Finally, we confront the MSR to classical measures for the calibration of common procedures such as color mapping and denoising. Overall, the MSR approach appears to be a good candidate for advanced image analysis and image processing, while providing a good level of physical interpretability.
我们使用一种不可知的信息论方法来研究自然图像的统计特性。我们引入多尺度相关性（MSR）度量来评估图像在所有尺度下对压缩的鲁棒性。从一个可控环境开始，我们将合成随机纹理的MSR表征为图像粗糙度[公式：见正文]和其他相关参数的函数。然后，我们将分析扩展到自然图像，并发现与临界（[公式：见正文]）随机纹理有惊人的相似之处。我们表明，与诸如功率谱分析等经典方法相比，MSR对图像内容更具鲁棒性且信息量更大。最后，我们将MSR与用于校准诸如颜色映射和去噪等常见过程的经典度量进行对比。总体而言，MSR方法似乎是高级图像分析和图像处理的一个很好的候选方法，同时提供了良好的物理可解释性水平。
