Special Section on Advances in Onboard Payload Data Compression

Performance impact of parameter tuning on the CCSDS-123 lossless multi- and hyperspectral image compression standard

[+] Author Affiliations
Estanislau Augé

Universitat Autònoma de Barcelona, Department of Information and Communications Engineering, Edifici Q, UAB, E-08193 Cerdanyola del Vallès, Barcelona, Spain

Jose Enrique Sánchez

Universitat Autònoma de Barcelona, Department of Information and Communications Engineering, Edifici Q, UAB, E-08193 Cerdanyola del Vallès, Barcelona, Spain

Aaron Kiely

California Institute of Technology, NASA Jet Propulsion Laboratory, Pasadena 91109, California

Ian Blanes

Universitat Autònoma de Barcelona, Department of Information and Communications Engineering, Edifici Q, UAB, E-08193 Cerdanyola del Vallès, Barcelona, Spain

Joan Serra-Sagristà

Universitat Autònoma de Barcelona, Department of Information and Communications Engineering, Edifici Q, UAB, E-08193 Cerdanyola del Vallès, Barcelona, Spain

J. Appl. Remote Sens. 7(1), 074594 (Aug 26, 2013). doi:10.1117/1.JRS.7.074594
History: Received January 3, 2013; Revised May 9, 2013; Accepted June 24, 2013
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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.

© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Estanislau Augé ; Jose Enrique Sánchez ; Aaron Kiely ; Ian Blanes and Joan Serra-Sagristà
"Performance impact of parameter tuning on the CCSDS-123 lossless multi- and hyperspectral image compression standard", J. Appl. Remote Sens. 7(1), 074594 (Aug 26, 2013). ; http://dx.doi.org/10.1117/1.JRS.7.074594


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