Near-Lossless Compression of Hyperspectral Images
Agnieszka C. Miguel, Jenny Liu, Dane K. Barney, Richard E. Ladner, E.A. Riskin · 2006
Research Supported By National Science Foundation Grant Number Ccr-0104800. Richard Ladner was supported in part by the Boeing Professor-ship in Computer Science and Engineering. Contact Information: Professor Agnieszka Miguel, Department of Electrical & Computer Engineering Seattle University, 901 12th Avenue, P.O. Box 222000, Seattle, WA 98122-1090, (206)296-5965, [email protected]. ABSTRACT Algorithms for near-lossless compression of hyperspectral images are presented. They guarantee that the intensity of any pixel in the decompressed image(s) differs from its original value by no more than a user-specified quantity. To reduce the bit rate required to code images while providing significantly more compression than lossless algorithms, linear prediction between the bands is used. Each band is predicted by a previously transmitted band. The prediction is subtracted from the original band, and the residual is compressed with a bit plane coder which uses context-based adaptive binary arithmetic coding. To find the best prediction algorithm, the impact of various band orderings and optimization techniques on the compression ratios is studied.