# Exploring Autoencoder-based Error-bounded Compression for Scientific Data

arXiv: https://arxiv.org/abs/2105.11730 · HTML: https://mlsanity.com/paper/2105.11730/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 62 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Lossy Image Compression with Compressive Autoencoders](https://mlsanity.com/paper/1703.00395/) (2017)
- [Variational Inference of Disentangled Latent Concepts from Unlabeled Observations](https://mlsanity.com/paper/1711.00848/) (2017)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [InfoVAE: Information Maximizing Variational Autoencoders](https://mlsanity.com/paper/1706.02262/) (2017)
- [Deep Convolutional AutoEncoder-based Lossy Image Compression](https://mlsanity.com/paper/1804.09535/) (2018)
- [Computationally Efficient Neural Image Compression](https://mlsanity.com/paper/1912.08771/) (2019)
- [Efficient Nonlinear Transforms for Lossy Image Compression](https://mlsanity.com/paper/1802.00847/) (2018)
- [cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2007.09625/) (2020)
- [SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors](https://mlsanity.com/paper/2101.03201/) (2021)
- [DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression](https://mlsanity.com/paper/1901.09124/) (2019)
- [Layered Image Compression using Scalable Auto-encoder](https://mlsanity.com/paper/1904.00553/) (2019)
- [Z-checker: A Framework for Assessing Lossy Compression of Scientific Data](https://mlsanity.com/paper/1707.09320/) (2017)
- [In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations](https://mlsanity.com/paper/1711.03888/) (2017)
- [Fixed-PSNR Lossy Compression for Scientific Data](https://mlsanity.com/paper/1805.07384/) (2018)

## Built on by (most cited first, 19 of 19)

- [Deep Hierarchical Super Resolution for Scientific Data](https://mlsanity.com/paper/2107.00462/) (2021)
- [IDLat: An Importance-Driven Latent Generation Method for Scientific Data](https://mlsanity.com/paper/2208.03345/) (2022)
- [SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks](https://mlsanity.com/paper/2309.04037/) (2023)
- [Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds](https://mlsanity.com/paper/2409.05357/) (2024)
- [Sparse L1-Autoencoders for Scientific Data Compression](https://mlsanity.com/paper/2405.14270/) (2024)
- [GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2404.13470/) (2024)
- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [Scalable Hybrid Learning Techniques for Scientific Data Compression](https://mlsanity.com/paper/2212.10733/) (2022)
- [Foundation Model for Lossy Compression of Spatiotemporal Scientific Data](https://mlsanity.com/paper/2412.17184/) (2024)
- [Residual Modeling for High-Fidelity Learned Compression of Scientific Data](https://mlsanity.com/paper/2606.05389/) (2026)
- [An autoencoder compression approach for accelerating large-scale inverse problems](https://mlsanity.com/paper/2304.04781/) (2023)
- [Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data](https://mlsanity.com/paper/2307.04216/) (2023)
- [A Geometric Lens on Physics-Aligned Data Compression](https://mlsanity.com/paper/2606.03279/) (2026)
- [Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications](https://mlsanity.com/paper/2409.18295/) (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)
- [Can Deep Neural Networks Improve Compression of Very Large Scientific Data?](https://mlsanity.com/paper/2606.14353/) (2026)
- [EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Database](https://mlsanity.com/paper/2607.18187/) (2026)
- [Neural Residual Modeling for Scientific Data Compression under Guaranteed Error Bounds](https://mlsanity.com/paper/2609.23185/) (2026)

## Abstract (arXiv)

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for the main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and compare them with six other related works. Experiments show that our solution exhibits a very competitive compression quality among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100% ~ 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.