CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessment

TL;DR

CSIM is a new image similarity metric based on copulas that is fast, sensitive to local changes, and outperforms existing metrics in detecting subtle image differences, especially useful in high-precision fields like medical imaging.

Contribution

The paper introduces CSIM, a novel copula-based similarity index that models pixel dependencies for improved sensitivity to local image variations.

Findings

CSIM outperforms traditional metrics in various distortion scenarios.

CSIM effectively detects subtle image differences such as noise and blur.

The method is suitable for high-precision applications like medical imaging.

Abstract

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.