# SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors

arXiv: https://arxiv.org/abs/2101.03201 · HTML: https://mlsanity.com/paper/2101.03201/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2021.

## Built on by (most cited first, 18 of 18)

- [Compressing multidimensional weather and climate data into neural networks](https://mlsanity.com/paper/2210.12538/) (2022)
- [Exploring Autoencoder-based Error-bounded Compression for Scientific Data](https://mlsanity.com/paper/2105.11730/) (2021)
- [A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images](https://mlsanity.com/paper/2502.21151/) (2025)
- [F-INR: Functional Tensor Decomposition for Implicit Neural Representations](https://mlsanity.com/paper/2503.21507/) (2025)
- [SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks](https://mlsanity.com/paper/2309.04037/) (2023)
- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression](https://mlsanity.com/paper/2208.07767/) (2022)
- [Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data](https://mlsanity.com/paper/2307.04216/) (2023)
- [JUMP-lite: Compact, reproducible benchmarking of cell representations](https://mlsanity.com/paper/2608.07632/) (2026)
- [SIMD Lossy Compression for Scientific Data](https://mlsanity.com/paper/2201.04614/) (2022)
- [Lessons Learned on the Path to Guaranteeing the Error Bound in Lossy Quantizers](https://mlsanity.com/paper/2407.15037/) (2024)
- [Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications](https://mlsanity.com/paper/2409.18295/) (2024)
- [Projected Tensor-Tensor Products for Efficient Computation of Optimal Multiway Data Representations](https://mlsanity.com/paper/2409.19402/) (2024)
- [A framework for compressing unstructured scientific data via serialization](https://mlsanity.com/paper/2410.08059/) (2024)
- [GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders](https://mlsanity.com/paper/2505.06316/) (2025)
- [LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)](https://mlsanity.com/paper/2510.23632/) (2025)
- [Efficient Learned Data Compression via Dual-Stream Feature Decoupling](https://mlsanity.com/paper/2604.07239/) (2026)
- [Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model](https://mlsanity.com/paper/2609.06008/) (2026)

## Abstract (arXiv)

Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produced by the applications. In the past decade, many lossless and lossy compressors have been developed with distinct design principles for different scientific datasets in largely diverse scientific domains. In order to support researchers and users assessing and comparing compressors in a fair and convenient way, we establish a standard compression assessment benchmark -- Scientific Data Reduction Benchmark (SDRBench). SDRBench contains a vast variety of real-world scientific datasets across different domains, summarizes several critical compression quality evaluation metrics, and integrates many state-of-the-art lossy and lossless compressors. We demonstrate evaluation results using SDRBench and summarize six valuable takeaways that are helpful to the in-depth understanding of lossy compressors.