AIRS (Atmospheric Infrared Sounder) images are a type of ultraspectral data cubes that are good candidates for compression as they include several thousand bands that account for well over 40MB of information per image. In this paper we describe and mathematically model an improved architecture to accomplish lossy compression of AIRS images by presenting a sequence of techniques executed under the context of preprocessing and compression stages. Specifically we describe both a preprocessing reversible stage that rearranges the AIRS data cube and a linear prediction based compression stage that improves the compression rate when compared to other state of the art ultraspectral data compression techniques. After defining a distortion measure as well as its effect on real applications (i.e. AIRS Level 2 products) we present a mathematical model to approximate the rate-distortion of the architecture and compare it against the experimental performance of the algorithm. The analysis relies on the vector quantization of the prediction error and assumes that the individual samples follow a Laplacian distribution that is the only source of distortion. In general under an open-loop encoding scheme, the distortion caused by the quantization of linear-prediction coefficients is masked by the distortion introduced by the prediction error itself. The effect of the preprocessing stage on the analytical model is accounted by different values of the Laplacian distribution such that the curve obtained by parametrically plotting rate against distortion is a close approximation of the experimental one.

Date Published: 18 May 2013
PDF: 11 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87430V (18 May 2013); doi: 10.1117/12.2017629

Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)