Image similarity metrics play an important role in computer vision applications, as they are used in image processing, computer vision and machine learning. Furthermore, those metrics enable tasks such as image retrieval, object recognition and quality assessment, essential in fields like healthcare, astronomy and surveillance. Existing metrics, such as PSNR, MSE, SSIM, ISSM and FSIM, often face limitations in terms of either speed, complexity or sensitivity to small changes in images. To address these challenges, a novel image similarity metric, namely CSIM, that combines real-time while being sensitive to subtle image variations is investigated in this paper. The novel metric uses Gaussian Copula from probability theory to transform an image into vectors of pixel distribution associated to local image patches. These vectors contain, in addition to intensities and pixel positions, information on the dependencies between pixel values, capturing the structural relationships within the image. By leveraging the properties of Copulas, CSIM effectively models the joint distribution of pixel intensities, enabling a more nuanced comparison of image patches making it more sensitive to local changes compared to other metrics. Experimental results demonstrate that CSIM outperforms existing similarity metrics in various image distortion scenarios, including noise, compression artifacts and blur. The metric's ability to detect subtle differences makes it suitable for applications requiring high precision, such as medical imaging, where the detection of minor anomalies can be of a high importance. The results obtained in this work can be reproduced from this Github repository: https://github.com/safouaneelg/copulasimilarity.

翻译：图像相似性度量在计算机视觉应用中扮演着重要角色，广泛应用于图像处理、计算机视觉和机器学习领域。此外，这些度量支持图像检索、目标识别和质量评估等任务，这些任务在医疗保健、天文学和监控等领域至关重要。现有度量指标，如PSNR、MSE、SSIM、ISSM和FSIM，通常在速度、复杂性或对图像微小变化的敏感性方面存在局限性。为应对这些挑战，本文研究了一种新颖的图像相似性度量方法，即CSIM，它结合了实时性，同时对细微的图像变化保持敏感。该新颖度量利用概率论中的高斯Copula，将图像转换为与局部图像块相关联的像素分布向量。这些向量除了包含强度和像素位置信息外，还包含像素值之间依赖关系的信息，从而捕捉图像内部的结构关系。通过利用Copula的性质，CSIM有效地建模了像素强度的联合分布，实现了对图像块更细致的比较，使其相比其他度量对局部变化更为敏感。实验结果表明，在包括噪声、压缩伪影和模糊在内的各种图像失真场景中，CSIM均优于现有的相似性度量。该度量检测细微差异的能力使其适用于需要高精度的应用，例如医学成像，其中微小异常的检测可能至关重要。本工作中获得的结果可从以下Github仓库复现：https://github.com/safouaneelg/copulasimilarity。