Statistics – Applications
Scientific paper
Oct 2007
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007spie.6700e...2o&link_type=abstract
Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications. Edited by Ritter, Gerh
Statistics
Applications
1
Scientific paper
Until now space telescopes, like Humbble, did not require a strong data compression. In fact, images were captured on demand and their proximity to Earth gave them a sufficient downlink bandwidth. Yet, the next generation space telescopes like GAIA (ESA) and the James Webb Space Telescope (JWST, ESA & NASA) will observe even wider sky fields at even higher resolutions. Moreover, they will be dramatically farther from Earth than Hubble (1.5 million versus 600 kilometers). This will imply a poor downlink bandwidth, and thus require a fast, on-board strong data compression (better than 1:200 ratios). To achieve GAIA scientific objectives, a real-time «selectively lossless» compression is needed. With standard schemes, it is simply not possible today, even without time constraints (because of the entropy limit...). This paper explains why the GAIA Compression, which is based on Object-Based Compression (OBC), is efficient for stellar images. Since the baseline implementation did not meet all the ESA requirements (compression speed and ratio), we have also brought our contribution to optimize the GAIA Compression. It consists mainly in using (i) non-rectangular regions for large objects and (ii) and (inter-objects) differential predictive coding to improve the effficiency of the final lossless compression. We have tested our algorithms on the GAIA sky generator (GIBIS) which stimulates flight-realistic conditioins (CCD read-noise, cosmic rays...). Without any loss on signal, we have obtained promising ratios up to 1:270 for the worst case sky.
Oseret Emmanuel
Timsit Claude
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