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This paper reports a comparative study of current lossless compression algorithms for data from a representative
selection of satellite based earth science multispectral imagers. The study includes the performance of compression
algorithms on Advanced Very High Resolution Radiometer(AVHRR), SEVIRI, the Moderate Resolution
Imaging Spectroradiometer(MODIS) imager, as well as a subset of MODIS bands as a proxy for the upcoming
GOES-R series. SEVIRI aboard the ESA/EUMETSAT operated Meteosat Second Generation (MSG) satellites
is a geostationary imager. The AVHRR aboard the NOAA Polar Orbiting Environmental Satellites and MODIS
aboard the NASA Terra and Aqua satellites have polar orbits. Thus this study will present representatives
from both polar and geostationary orbiting imagers. The imagers we include have sensors for both reflected
and emissive radiance. We also note that the older satellites have coarser quantizations and present our conclusions
on the impact on compression ratios. Faced with a enormous growing large volume of data on a new
emerging current generation images from faster scanning, finer spatial resolution, and greater spectral resolution,
this study provides a comparison of current compression algorithms as a baseline for future work. With
growing satellite Earth science multispectral imager volume data, it becomes increasingly important to evaluate
which compression algorithms are most appropriate for data management in transmission and archiving. This
comparative compression study uses a wide range standard implementations of the leading lossless compression
algorithms. Examples include image compression algorithms such as PNG and JPEG2000, and widely-used file
compression formats such as BZIP2 and 7z. This study includes a comparison with the Consultative Committee
for Space Data Systems (CCSDS) recommended Szip software which uses the extended-Rice lossless compression
algorithm as well as the most recent recommended compression standard which relies on a wavelet transform
followed by an entropy coder. To establish statistical significance of our analysis, we have developed a system to
acquire and manage a large number of imager granules: currently over 1000 MODIS granules, over 2400 AVHRR
granules, and over 220 SEVIRI granules.
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