# DSSIM: a structural similarity index for floating-point data

arXiv: https://arxiv.org/abs/2202.02616 · HTML: https://mlsanity.com/paper/2202.02616/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 5 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Understanding SSIM](https://mlsanity.com/paper/2006.13846/) (2020)
- [A Hitchhiker's Guide to Structural Similarity](https://mlsanity.com/paper/2101.06354/) (2021)
- [Discriminability Tests for Visualization Effectiveness and Scalability](https://mlsanity.com/paper/1907.11358/) (2019)

## Built on by (most cited first, 10 of 10)

- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation](https://mlsanity.com/paper/2501.00637/) (2024)
- [Lossy Neural Compression for Geospatial Analytics: A Review](https://mlsanity.com/paper/2503.01505/) (2025)
- [Distributed Neural Representation for Reactive in situ Visualization](https://mlsanity.com/paper/2304.10516/) (2023)
- [ContactGaussian-WM: Learning Physics-Grounded World Model from Videos](https://mlsanity.com/paper/2602.11021/) (2026)
- [Flash-Splat: 3D Reflection Removal with Flash Cues and Gaussian Splats](https://mlsanity.com/paper/2410.02764/) (2024)
- [A review on Machine Learning based User-Centric Multimedia Streaming Techniques](https://mlsanity.com/paper/2411.15801/) (2024)
- [WaveSim: A Wavelet-based Multi-scale Similarity Metric for Weather and Climate Fields](https://mlsanity.com/paper/2512.14656/) (2025)
- [GS-2M: Material-aware Gaussian Splatting for High-fidelity Mesh Reconstruction](https://mlsanity.com/paper/2509.22276/) (2025)
- [Pi-GS: Sparse-View Gaussian Splatting with Dense π3 Initialization](https://mlsanity.com/paper/2602.03327/) (2026)

## Abstract (arXiv)

Data visualization is a critical component in terms of interacting with floating-point output data from large model simulation codes. Indeed, postprocessing analysis workflows on simulation data often generate a large number of images from the raw data, many of which are then compared to each other or to specified reference images. In this image-comparison scenario, image quality assessment (IQA) measures are quite useful, and the Structural Similarity Index (SSIM) continues to be a popular choice. However, generating large numbers of images can be costly, and plot-specific (but data independent) choices can affect the SSIM value. A natural question is whether we can apply the SSIM directly to the floating-point simulation data and obtain an indication of whether differences in the data are likely to impact a visual assessment, effectively bypassing the creation of a specific set of images from the data. To this end, we propose an alternative to the popular SSIM that can be applied directly to the floating point data, which we refer to as the Data SSIM (DSSIM). While we demonstrate the usefulness of the DSSIM in the context of evaluating differences due to lossy compression on large volumes of simulation data from a popular climate model, the DSSIM may prove useful for many other applications involving simulation or image data.