Keywords: hyperspectral imaging, lossless compression, adaptive prediction, wiener filtering, context-based arithmetic coding
Received June 29, 2009; revised December 3, 2009; accepted January 20, 2010.
Communicated by Tyng-Luh Liu.
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CHENG-CHEN LIN AND YIN-TSUNG HWANG
Department of Electrical Engineering
National Chung Hsing University
Taichung, 402 Taiwan
In this paper, an effective lossless compression scheme for hyperspectral images is
presented. The proposed scheme is based on a table look-up approach in prediction and
employs two novel measures to improve the compression performance. The first measure
takes advantage of the spatial data correlation and formulates the derivation of a spectral
domain predictor as a process of Wiener filtering. The derived predictor is considered statistically
optimal provided that the data within a small context window are stationary. This
property holds in most cases due to spatial data correlation. Under the Wiener filtering
framework, the proposed predictor can be extended from one-tap to multi-tap prediction to
further improve performance. In the second measure, a backward search scheme is used
instead of look-up tables, which reduces the memory storage requirement drastically and
achieves performance equivalent to that obtained using multiple look-up tables. The search
effort is greatly reduced using the quantization index approach. Simulations on parameter
settings and refinements on entropy coding are conducted to fine-tune performance. Experiments
on 5 sequences of AVIRIS images show that the proposed scheme can yield an
average compression ratio of as high as 3.85.
Received June 29, 2009; revised December 3, 2009; accepted January 20, 2010.
Communicated by Tyng-Luh Liu.