SolarZip: An Efficient and Adaptive Compression Framework for Solar EUV Imaging Data

TL;DR

SolarZip addresses the data-deluge challenge in solar EUV imaging by introducing an adaptive, error-bounded lossy compression framework tailored to Solar Orbiter EUI data. It combines a hybrid strategy with enhanced spline interpolation predictors and a two-stage evaluation that integrates distortion metrics with domain-specific post hoc analyses to ensure scientific usability. The framework achieves unprecedented compression ratios (up to $800\times$ for FSI and $500\times$ for HRI_EUV) and can reduce data transmission time by up to $270\times$, while preserving key solar structures and features. This approach provides a practical data management solution for deep-space missions and demonstrates the value of adaptive, observation-driven compression for complex, dynamic astronomical data.

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 develop an efficient compression and evaluation framework for solar EUV data, specifically optimized for Solar Orbiter Extreme Ultraviolet Imager (EUI) data, significantly reducing data volume while preserving scientific usability. Methods: We systematically evaluated four error-bounded lossy compressors across two EUI datasets. However, the existing methods cannot perfectly handle the EUI datasets (with continuously changing distance and significant resolution differences). Motivated by this, we develop an adaptive hybrid compression strategy with optimized interpolation predictors. Moreover, we designed a two-stage evaluation framework integrating distortion analysis with downstream scientific workflows, ensuring that observational analysis is not affected at high compression ratios. Results: Our framework SolarZip achieved up to 800x reduction for Full Sun Imager (FSI) data and 500x for High Resolution Imager (HRI$_{\text{EUV}}$) data. It significantly outperformed both traditional and advanced algorithms, achieving 3-50x 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 270x 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 optimal compression methods based on observational scenarios and user requirements. This provides a promising data management solution for deep space missions like Solar Orbiter.

Figures (14)

• Figure 1: Life cycle of Solar Orbiter observation data. This figure illustrates the application of data compression techniques throughout the lifecycle of solar observation data. These techniques significantly reduce data volume, addressing communication and storage challenges, particularly for on-board systems and data centers.
• Figure 2: Panel a: Visualization of the Solar Orbiter’s trajectory based on the complete FSI dataset. Panel b: Compression ratio trends over time at a fixed quality level corresponding to a PSNR = 88 and PSNR = 75. Color coding indicates different compression methods. RICE and lossless compression (GZip) have fixed compression ratios and are used as baselines for reference. Advanced lossy compressors demonstrate significant advantages in compression ratio. Panel c: Visual comparison and difference maps between the original image (acquired on April 5, 2024) and the reconstructed images from five compression algorithms. All difference map display ranges were set to ±50 for consistency in this work.
• Figure 3: Processing pipeline of EUI data (using FSI data as an example). The on-board WICOM compression (similar to JPEG2000) is mostly lossy, with only a small portion using lossless compression. Subsequent processing is performed on the ground.
• Figure 4: Overview of SolarZip framework. The system consists of three stages: preprocessing, compression, and analysis. The core algorithmic innovation lies in the strategy controller, which can automatically tune and select the optimal compression strategy. The subsequent two stages of comprehensive analysis ensure that the decompressed data remain suitable for scientific purposes.
• Figure 5: Steps of the adaptive hybrid compression strategy. Left panel shows the compression strategy under relaxed error bounds, while the right panel shows strategy under strict error bounds. Our method dynamically selects the optimal compression strategy and optimize it. The different strategies are denoted by S1–S4 in the figure.