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

arXiv: https://arxiv.org/abs/2410.01411 · HTML: https://mlsanity.com/paper/2410.01411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models](https://mlsanity.com/paper/2304.02742/) (2023)
- [Learning to Generate Images with Perceptual Similarity Metrics](https://mlsanity.com/paper/1511.06409/) (2015)
- [A Loss Function for Generative Neural Networks Based on Watson's Perceptual Model](https://mlsanity.com/paper/2006.15057/) (2020)
- [Learning Image and Video Compression through Spatial-Temporal Energy Compaction](https://mlsanity.com/paper/1906.09683/) (2019)
- [Color Texture Image Retrieval Based on Copula Multivariate Modeling in the Shearlet Domain](https://mlsanity.com/paper/2008.00910/) (2020)
- [(Re-)reading Sklar (1959) -- A personal view on Sklar's theorem](https://mlsanity.com/paper/2312.08570/) (2023)

## Built on by (most cited first, 2 of 2)

- [EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers](https://mlsanity.com/paper/2601.22127/) (2026)
- [UniSync: Towards Generalizable and High-Fidelity Lip Synchronization for Challenging Scenarios](https://mlsanity.com/paper/2603.03882/) (2026)

## Abstract (arXiv)

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.