AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data (2506.08306v1)

Abstract: The site conditions that make astronomical observatories in space and on the ground so desirable -- cold and dark -- demand a physical remoteness that leads to limited data transmission capabilities. Such transmission limitations directly bottleneck the amount of data acquired and in an era of costly modern observatories, any improvements in lossless data compression has the potential scale to billions of dollars worth of additional science that can be accomplished on the same instrument. Traditional lossless methods for compressing astrophysical data are manually designed. Neural data compression, on the other hand, holds the promise of learning compression algorithms end-to-end from data and outperforming classical techniques by leveraging the unique spatial, temporal, and wavelength structures of astronomical images. This paper introduces AstroCompress: a neural compression challenge for astrophysics data, featuring four new datasets (and one legacy dataset) with 16-bit unsigned integer imaging data in various modes: space-based, ground-based, multi-wavelength, and time-series imaging. We provide code to easily access the data and benchmark seven lossless compression methods (three neural and four non-neural, including all practical state-of-the-art algorithms). Our results on lossless compression indicate that lossless neural compression techniques can enhance data collection at observatories, and provide guidance on the adoption of neural compression in scientific applications. Though the scope of this paper is restricted to lossless compression, we also comment on the potential exploration of lossy compression methods in future studies.

• The paper introduces a curated dataset combining diverse astronomical imaging conditions to evaluate neural compression techniques.
• It compares neural models such as IDF and PixelCNN++ against traditional methods, highlighting competitive performance and practical applicability.
• The research emphasizes the role of noise characteristics and dataset diversity in achieving higher compression efficiency for astronomical imagery.

AstroCompress: A Benchmark Dataset for Lossless Compression of Astronomical Imaging

AstroCompress represents a noteworthy initiative aimed at addressing data transmission limitations in astronomical observatories by leveraging neural compression techniques. The research articulates the challenges posed by the vast data output and bandwidth constraints inherent to modern astronomical surveys, both ground-based and space-based. Conventional lossless compression methods, often manually designed, struggle with the unique attributes of astronomical imagery, including spatial, temporal, and wavelength-specific structures. AstroCompress introduces a curated dataset designed to facilitate the application of neural compression algorithms, offering potential improvements in data transmission efficiency.

Dataset Composition and Characteristics

AstroCompress consists of five datasets representing varied imaging conditions and technological specifications. These datasets include:

1. GBI-16-2D (Keck): This dataset comprises optical imaging data from different filters and exposure times, utilizing CCD detectors.
2. SBI-16-2D (Hubble): Derived from the Hubble Space Telescope using the ACS instrument, it features challenges such as cosmic ray noise and charge-transfer inefficiency.
3. SBI-16-3D (JWST): Featuring time-series imaging from JWST’s NIRCAM instrument, this dataset allows exploration of residual coding due to its repetitive temporal sampling.
4. GBI-16-4D (SDSS): Composing 4D cubes from the SDSS survey, this encompasses imaging of the same sky patch across multiple wavelengths and time steps.
5. GBI-16-2D-Legacy: A smaller dataset from various ground-based observatories, primarily used for verifying compression techniques.

Compression Methodologies

The paper evaluates both neural and non-neural lossless compression techniques:

• Neural Methods include models such as Integer Discrete Flows (IDF), L3C, and PixelCNN++, which utilize various deep generative modeling strategies.
• Non-Neural Baselines: Includes traditional approaches like JPEG-XL and JPEG-2000, with JPEG-XL setting a new standard amongst non-neural methods according to the paper results.

Experimental Results

The research reveals several critical insights:

• Neural methods, particularly IDF and PixelCNN++, achieved competitive compression ratios compared to non-neural counterparts.
• Non-neural JPEG-XL (max effort) consistently demonstrated dominant compression ratio performance, suggesting its utility in practical applications.
• Spectrally and temporally correlated datasets showed potential for higher compression ratios, a result not consistently capitalized upon by current neural methods.
• Noise levels significantly impact compressibility, aligning with Shannon’s source coding theorem concerning entropy and the Gaussian distribution of background noise.

Generalization and Runtime Analysis

Generalization experiments underscored the importance of dataset diversity in training effective compression models, suggesting that broad multi-modal datasets could enhance model robustness across varied astronomical imaging tasks. Additionally, runtime metrics highlighted the computational feasibility of neural methods, indicating areas for future optimization to meet practical constraints in astronomical data processing.

Future Directions and Implications

AstroCompress sets a foundation for future exploration in both lossless and lossy compression, with lossy approaches holding promise for substantial gains given the predominantly noisy pixels in astronomical imagery. As astronomical datasets transition into exabyte scales, efficient data handling paradigms, bolstered by developments in AI and machine learning, could redefine data processing pipelines, providing both economic and scientific value.

AstroCompress emphasizes the need for tailored compression solutions that balance specificity and generalizability, underscoring the pivotal role of collaborative efforts between astronomers and computer scientists. Looking forward, advancements in hardware support and algorithmic design tailored for astronomical contexts are poised to address the impending data deluge effectively.

In conclusion, while successful neural compression models offer significant promise, ongoing improvements along computational and methodological dimensions remain essential to fully realize the potential of neural compression technologies in astronomical applications. AstroCompress stands as a crucial step towards this goal, offering a robust and well-characterized dataset to stimulate further research and practical advancements in the compression of astronomical imagery.