Predictive Coding of Hyperspectral Images
Agnieszka C. Miguel, Richard E. Ladner, E.A. Riskin, Scott Hauck, Dane K. Barney, ‡Amanda R. Askew, Alexander C. Chang · Kluwer Academic Publishers eBooks · 2006
Algorithms for lossless and lossy compression of hyperspectral images are presented. To greatly reduce the bit rate required to code images and to exploit the large amount of inter-band correlation, linear prediction between the bands is used. Each band, except the first one, is predicted by previously transmitted band. Once the prediction is formed, it is subtracted from the original ∗This work appeared in part in the Proceedings of the NASA Earth Science Technology Conference, 2003, and in the Proceedings of the Data Compression Conference, 2004. Research supported by NASA Contract NAS5-00213 and National Science Foundation grant number CCR-0104800. Scott Hauck was supported in part by an NSF CAREER Award and an Alfred P. Sloan Research Fellowship. Contact information: Professor Richard Ladner, University of Washington, Box 352500, Seattle, WA 981952500, (206) 543-9347, [email protected]. band, and the residual (difference image) is compressed. To find the best prediction algorithm, the impact of various band orderings and measures of prediction quality on the compression ratios is studied. The resulting lossless compression algorithm displays performance that is comparable with other recently published results. To reduce the complexity of the lossy predictive encoder, a bit plane-synchronized closed loop predictor that does not require full decompression of a previous band at the encoder is proposed. The new technique achieves similar compression ratios to that of standard closed loop predictive coding and has a simpler implementation.