SolarZip: An efficient and adaptive compression framework for Solar EUV imaging data

Application to Solar Orbiter/EUI data

¹ Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
² University of Electronic Science and Technology of China, Chengdu 610054, China
³ Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany
⁴ Institut für Physik und Astronomie, Universität Potsdam, Karl-Liebknecht-Straße 24/25, 14476 Potsdam, Germany
⁵ University of Chinese Academy of Sciences, Beijing 100049, China
⁶ Minzu University of China, Beijing 100081, China
⁷ Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
⁸ Centre for Astronomical Mega-Science, Chinese Academy of Sciences, Beijing 100012, China

^⋆⋆ Corresponding author: taodingwen@ict.ac.cn

Received: 17 April 2025
Accepted: 25 July 2025

Abstract

Context. With the advancement of solar physics research, next-generation solar space missions and ground-based telescopes face significant challenges in efficiently transmitting and/or storing large-scale observational data.

Aims. We have developed an efficient compression and evaluation framework for solar EUV data, specifically optimized for Solar Orbiter Extreme Ultraviolet Imager (EUI) data. It significantly reduces the data volume, while preserving scientific usability.

Methods. We evaluated four error-bounded lossy compressors across two Solar Orbiter/EUI datasets spanning 50 months of observations. However, existing methods cannot perfectly handle the EUV data. Building on this analysis, we developed SolarZip, an adaptive compression framework featuring: (1) a hybrid strategy controller that dynamically selects the optimal compression strategy; (2) enhanced spline interpolation predictors with grid-wise anchor points and level-wise error bound auto-tuning; and (3) a comprehensive two-stage evaluation methodology integrating standard distortion metrics with domain-specific post hoc scientific analyses.

Results. Our SolarZip framework achieved a data compression ratio of up to 800× for Full Sun Imager (FSI) data and 500× for High Resolution Imager (HRI_EUV) data. It significantly outperformed both traditional and advanced algorithms, achieving 3−50× higher compression ratios than traditional algorithms, surpassing the second-best algorithm by up to 30%. Simulation experiments verified that SolarZip can reduce data transmission time by up to 270× while ensuring the preservation of scientific usability.

Conclusions. The SolarZip framework significantly enhances solar observational data compression efficiency, while preserving scientific usability by dynamically selecting the optimal compression methods based on observational scenarios and user requirements. This approach offers a promising data management solution for deep space missions, such as Solar Orbiter.