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Journal of Applied Remote Sensing

Performance impact of parameter tuning on the CCSDS-123 lossless multi- and hyperspectral image compression standard
Author(s): Estanislau Augé; Jose Enrique Sánchez; Aaron B. Kiely; Ian Blanes; Joan Serra-Sagrista
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Paper Abstract

Multi-spectral and hyperspectral image data payloads have large size and may be challenging to download from remote sensors. To alleviate this problem, such images can be effectively compressed using specially designed algorithms. The new CCSDS-123 standard has been developed to address onboard lossless coding of multi-spectral and hyperspectral images. The standard is based on the fast lossless algorithm, which is composed of a causal context-based prediction stage and an entropy-coding stage that utilizes Golomb power-of-two codes. Several parts of each of these two stages have adjustable parameters. CCSDS-123 provides satisfactory performance for a wide set of imagery acquired by various sensors; but end-users of a CCSDS-123 implementation may require assistance to select a suitable combination of parameters for a specific application scenario. To assist end-users, this paper investigates the performance of CCSDS-123 under different parameter combinations and addresses the selection of an adequate combination given a specific sensor. Experimental results suggest that prediction parameters have a greater impact on the compression performance than entropy-coding parameters.

Paper Details

Date Published: 26 August 2013
PDF: 16 pages
J. Appl. Rem. Sens. 7(1) 074594 doi: 10.1117/1.JRS.7.074594
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Estanislau Augé, Univ. Autònoma de Barcelona (Spain)
Jose Enrique Sánchez, Univ. Autònoma de Barcelona (Spain)
Aaron B. Kiely, Jet Propulsion Lab. (United States)
Ian Blanes, Univ. Autònoma de Barcelona (Spain)
Joan Serra-Sagrista, Univ. Autònoma de Barcelona (Spain)

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