| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03073 | |
| Number of page(s) | 9 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103073 | |
| Published online | 23 August 2021 | |
https://doi.org/10.1051/epjconf/202125103073
Intelligent compression for synchrotron radiation source image
1 Institute of High Energy Physics, CAS, 100049 Beijing, China
2 University of Chinese Academy of Sciences, 100049 Beijing, China
3 Tianfu Cosmic Ray Research Center, Institute of High Energy Physics, Chinese Academy of Sciences, 610041 Chengdu, China
* Corresponding author: fusy@ihep.ac.cn
Published online: 23 August 2021
Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we propose a lossless compression method for SRS images based on deep learning. First, we use the difference algorithm to reduce the linear correlation within the image sequence. Then we propose a reversible truncated mapping method to reduce the range of the pixel value distribution. Thirdly, we train a deep learning model to learn the nonlinear relationship within the image sequence. Finally, we use the probability distribution predicted by the deep leaning model combined with arithmetic coding to fulfil lossless compression. Test result based on SRS images shows that our method can further decrease 20% of the data size compared to PNG, JPEG2000 and FLIF.
© The Authors, published by EDP Sciences, 2021
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