Image and spectral image compression for four experiments on the ROSETTA and Mars Express missions of ESA

Details Image and spectral image compression for four experiments on the ROSETTA and Mars Express missions of ESA Image and spectral image compression for four experiments on the ROSETTA and Mars Express missions of ESA

Dec 2000

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000spie.4115..364l&link_type=abstract

Proc. SPIE Vol. 4115, p. 364-373, Applications of Digital Image Processing XXIII, Andrew G. Tescher; Ed.

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The output rates of imaging scientific experiments on planetary missions far exceed the few 10 kbits/s provided by X or Ka band downlink. This severely restricts the duration and frequency of observations. Space applications present specific constraints for compression methods: space qualified ROM and fast RAM chips have limited capacity and large power requirements. Real time compression is therefore preferable (no large local data buffer) but requires a large processing throughput. Wavelet compression provides a fast and efficient method for lossy data compression, when combined with tree- coding algorithms such as that of Said and Pearlman. We have developed such an algorithm for four instruments on ROSETTA (ESA cometary rendez-vous mission) and Mars Express (ESA Mars Orbiter and Lander mission), building on the experience from two experiments on CASSINI and MARS 96 for which lossless compression was implemented. Modern Digital Signal Processors using a pipeline architecture provide the required high computing capability. The Said-Pearlman tree-coding algorithm has been optimized for speed and code size by reducing branching and bit manipulation, which are very costly in terms of processor cycles. Written in C with a few assembly language modules, the implementation on a DSP of this new version of the Said-Pearlman algorithm provides a processing capability of 500 kdata/s (imaging), which is adequate for our applications. Compression ratios of at least 10 can be achieved with acceptable data quality.

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