# GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data

arXiv: https://arxiv.org/abs/2404.13470 · HTML: https://mlsanity.com/paper/2404.13470/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Exploring Autoencoder-based Error-bounded Compression for Scientific Data](https://mlsanity.com/paper/2105.11730/) (2021)
- [MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction](https://mlsanity.com/paper/2010.05872/) (2020)
- [High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation](https://mlsanity.com/paper/2311.12133/) (2023)
- [SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks](https://mlsanity.com/paper/2309.04037/) (2023)
- [Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP](https://mlsanity.com/paper/1806.08901/) (2018)
- [Z-checker: A Framework for Assessing Lossy Compression of Scientific Data](https://mlsanity.com/paper/1707.09320/) (2017)
- [Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets](https://mlsanity.com/paper/2310.14133/) (2023)

## Built on by (most cited first, 3 of 3)

- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications](https://mlsanity.com/paper/2409.18295/) (2024)
- [Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds](https://mlsanity.com/paper/2609.23185/) (2026)

## Abstract (arXiv)

The rapid expansion of computational capabilities and the ever-growing scale of modern HPC systems present formidable challenges in managing exascale scientific data. Faced with such vast datasets, traditional lossless compression techniques prove insufficient in reducing data size to a manageable level while preserving all information intact. In response, researchers have turned to error-bounded lossy compression methods, which offer a balance between data size reduction and information retention. However, despite their utility, these compressors employing conventional techniques struggle with limited reconstruction quality. To address this issue, we draw inspiration from recent advancements in deep learning and propose GWLZ, a novel group-wise learning-based lossy compression framework with multiple lightweight learnable enhancer models. Leveraging a group of neural networks, GWLZ significantly enhances the decompressed data reconstruction quality with negligible impact on the compression efficiency. Experimental results on different fields from the Nyx dataset demonstrate remarkable improvements by GWLZ, achieving up to 20% quality enhancements with negligible overhead as low as 0.0003x.