SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
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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.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jan 2021.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 4; 2025: 4; 2026: 3.
16 papers.
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- 2022Compressing multidimensional weather and climate data into neural networkscited by 31
- 2021Exploring Autoencoder-based Error-bounded Compression for Scientific Datacited by 19
- 2025A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Imagescited by 8
- 2025F-INR: Functional Tensor Decomposition for Implicit Neural Representationscited by 6
- 2023SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networkscited by 5
- 2024NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compressioncited by 3
- 2022Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compressioncited by 1
- 2023Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Datacited by 1
- 2024Enhancing Lossy Compression Through Cross-Field Information for Scientific Applicationscited by 0
- 2024Projected Tensor-Tensor Products for Efficient Computation of Optimal Multiway Data Representationscited by 0
- 2024A framework for compressing unstructured scientific data via serializationcited by 0
- 2025GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoderscited by 0
4 more
- 2025LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)cited by 0
- 2026Efficient Learned Data Compression via Dual-Stream Feature Decouplingcited by 0
- 2026JUMP-lite: Compact, reproducible benchmarking of cell representationscited by 0
- 2026Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Modelcited by 0
- 2026Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model5 Sep 2026
- 2026JUMP-lite: Compact, reproducible benchmarking of cell representations7 Aug 2026
- 2026Efficient Learned Data Compression via Dual-Stream Feature Decoupling8 Apr 2026
- 2025LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)24 Oct 2025
- 2025GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders8 May 2025
- 2025F-INR: Functional Tensor Decomposition for Implicit Neural Representations27 Mar 2025
- 2025A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images28 Feb 2025
- 2024A framework for compressing unstructured scientific data via serialization10 Oct 2024
- 2024Projected Tensor-Tensor Products for Efficient Computation of Optimal Multiway Data Representations28 Sep 2024
- 2024Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications26 Sep 2024
- 2024NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression9 Sep 2024
- 2023SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks7 Sep 2023
4 more
- 2023Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data9 Jul 2023
- 2022Compressing multidimensional weather and climate data into neural networks22 Oct 2022
- 2022Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression16 Aug 2022
- 2021Exploring Autoencoder-based Error-bounded Compression for Scientific Data25 May 2021
- 2022Compressing multidimensional weather and climate data into neural networkscited by 31
- 2021Exploring Autoencoder-based Error-bounded Compression for Scientific Datacited by 19
- 2025A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Imagescited by 8
- 2025F-INR: Functional Tensor Decomposition for Implicit Neural Representationscited by 6
- 2023SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networkscited by 5
- 2024NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compressioncited by 3
- 2022Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compressioncited by 1
- 2023Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Datacited by 1
- 2024Enhancing Lossy Compression Through Cross-Field Information for Scientific Applicationscited by 0
- 2024Projected Tensor-Tensor Products for Efficient Computation of Optimal Multiway Data Representationscited by 0
- 2024A framework for compressing unstructured scientific data via serializationcited by 0
- 2025GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoderscited by 0
12 most cited of 16 All 16 (JSON)
- 2026Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model5 Sep 2026
- 2026JUMP-lite: Compact, reproducible benchmarking of cell representations7 Aug 2026
- 2026Efficient Learned Data Compression via Dual-Stream Feature Decoupling8 Apr 2026
- 2025LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)24 Oct 2025
- 2025GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders8 May 2025
- 2025F-INR: Functional Tensor Decomposition for Implicit Neural Representations27 Mar 2025
- 2025A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images28 Feb 2025
- 2024A framework for compressing unstructured scientific data via serialization10 Oct 2024
- 2024Projected Tensor-Tensor Products for Efficient Computation of Optimal Multiway Data Representations28 Sep 2024
- 2024Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications26 Sep 2024
- 2024NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression9 Sep 2024
- 2023SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks7 Sep 2023
12 newest of 16 All 16 (JSON)