Capturing global redundancy to improve compression of large images

Author

Kess, Barbara L. ; Reichenbach, Stephen E.

Author_Institution

Dept. of Comput. Sci. & Eng., Nebraska Univ., Lincoln, NE, USA

fYear

1997

fDate

25-27 Mar 1997

Firstpage

62

Lastpage

71

Abstract

A Source Specific Model for Global Earth Data (SSM-GED) is a lossless compression method for large images that captures global redundancy in the data and achieves a significant improvement over CALIC and DCXT-BT/CARP, two leading lossless compression schemes. The Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) data, which contains 662 Megabytes (MB) per band, is an example of a large data set that requires decompression of regions of the data. For this reason, SSM-GED compresses the AVHRR data as a collection of subwindows. This approach defines the statistical parameters for the model prior to compression. Unlike universal models that assume no a priori knowledge of the data, SSM-GED captures global redundancy that exists among all of the subwindows of data. The overlap in parameters among subwindows of data enables SSM-GED to improve the compression rate by increasing the number of parameters and maintaining a small model cost for each subwindow of data

Keywords

data compression; image coding; radiometry; redundancy; remote sensing; Global Land 1-km Advanced Very High Resolution Radiometer data; Source Specific Model for Global Earth Data; data subwindows; global redundancy; large images; lossless compression method; remote sensing; statistical parameters; Computer science; Costs; Data engineering; Geoscience; Image coding; NASA; Presses; Radiofrequency interference; Radiometry; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Compression Conference, 1997. DCC '97. Proceedings

Conference_Location

Snowbird, UT

ISSN

1068-0314

Print_ISBN

0-8186-7761-9

Type

conf

DOI

10.1109/DCC.1997.581967

Filename

581967