AI-based Compression of SDO/AIA for Solar Coronal Features

Abstract

In this study, we apply AI-based image compression methods to the Atmospheric Imaging Assembly (AIA) data on board the Solar Dynamics Observatory (SDO). We apply pretrained variational autoencoder (VAE) models as well as versions fine-tuned to solar extreme ultraviolet (EUV) data. We evaluate quality metrics such as peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index measure (MS-SSIM) for human perception ability as a function of compression rates, represented by bits per pixel (bpp). Additionally, we analyze the differential emission measure (DEM) and pixel intensity variation of coronal features such as coronal loops and bright points and compare coronal loop oscillations between the original and decompressed data. We compare these results with conventional methods such as the JPEG method and the RICE algorithm commonly applied to solar spacecraft data. The major results from this study are as follows. First, AI-based image compression methods show higher MS-SSIM values than the conventional methods, at similar compression rates. Second, the DEM curves from the AI-based decompressed data generally follow the main trend of the original data. Third, the pixel intensity variations show wavelength-dependent differences, but the decompressed data generally retain the main trend of the original intensity variations. Fourth, the coronal loop oscillation obtained from the decompressed data is consistent with that of the observed coronal loop in the original data. We hope that AI-based image compression methods can effectively handle large amounts of solar data for future deep space missions and data storage.

Unified Scaling Relation for Coronal Temperature Anomaly and Solar Wind Acceleration
Shuai Sheng
EGUsphere4 Nov 2026PreprintCC BY 4.0Full text1 citedSolar & stellar physicsEGUsphere
First Observation of a Polar Coronal Hole-like Fast Solar Wind Stream in the Sub-Alfvénic Solar Corona: an Analysis of Turbulence Properties
Riddhi Bandyopadhyay, Sujan Prasad Gautam, William H. Matthaeus +2
arXiv22 Sep 2026PreprintCC BY 4.0Full textSolar & stellar physics Plasma physicsarXiv
The First Observation of a Polar Coronal Hole-like Fast Solar Wind Stream in the Sub-Alfvénic Solar Corona: An Analysis of Turbulence Properties
Riddhi Bandyopadhyay, Sujan Prasad Gautam, William H. Matthaeus +2
The Astrophysical Journal Letters8 Oct 2026Peer-reviewedCC BY 4.0Full textSolar & stellar physicsCrossref
Influence of local and global solar magnetic fields on medium-term forecasting of the number and parameters of coronal mass ejections
Irina A. Bilenko
arXiv17 Oct 2026PreprintCC BY 4.0Full textSolar & stellar physicsarXiv
Solar Orbiter’s Far-side Magnetogram Impacts on Coronal Holes and Solar Wind Predictions
Evangelia Samara, Charles N. Arge, Samuel J. Schonfeld +5
The Astrophysical Journal30 Oct 2026Peer-reviewedCC BY 4.0Full textSolar & stellar physicsIOP/AASCrossref

Cite

BibTeX RIS

BibTeX

@article{lee2026aibased,
  title = {{AI-based Compression of SDO/AIA for Solar Coronal Features}},
  author = {Jaewon Lee and Yong-Jae Moon and Seong-Gyeong Jeon and Youngjae Kim and Daeil Kim},
  journal = {The Astrophysical Journal},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4357/aea07c},
  url = {https://iopscience.iop.org/article/10.3847/1538-4357/aea07c},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - AI-based Compression of SDO/AIA for Solar Coronal Features
AU  - Jaewon Lee
AU  - Yong-Jae Moon
AU  - Seong-Gyeong Jeon
AU  - Youngjae Kim
AU  - Daeil Kim
PY  - 2026
DA  - 2026/10/28
JO  - The Astrophysical Journal
DO  - 10.3847/1538-4357/aea07c
UR  - https://iopscience.iop.org/article/10.3847/1538-4357/aea07c
AB  - In this study, we apply AI-based image compression methods to the Atmospheric Imaging Assembly (AIA) data on board the Solar Dynamics Observatory (SDO). We apply pretrained variational autoencoder (VAE) models as well as versions fine-tuned to solar extreme ultraviolet (EUV) data. We evaluate quality metrics such as peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index measure (MS-SSIM) for human perception ability as a function of compression rates, represented by bits per pixel (bpp). Additionally, we analyze the differential emission measure (DEM) and pixel intensity variation of coronal features such as coronal loops and bright points and compare coronal loop oscillations between the original and decompressed data. We compare these results with conventional methods such as the JPEG method and the RICE algorithm commonly applied to solar spacecraft data. The major results from this study are as follows. First, AI-based image compression methods show higher MS-SSIM values than the conventional methods, at similar compression rates. Second, the DEM curves from the AI-based decompressed data generally follow the main trend of the original data. Third, the pixel intensity variations show wavelength-dependent differences, but the decompressed data generally retain the main trend of the original intensity variations. Fourth, the coronal loop oscillation obtained from the decompressed data is consistent with that of the observed coronal loop in the original data. We hope that AI-based image compression methods can effectively handle large amounts of solar data for future deep space missions and data storage.
SN  - 1538-4357
SN  - 0004-637X
ER  -

Abstract

Related

Cite