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

Estanislau Augé, Jose Enrique Sánchez, Aaron Kiely, Ian Blanes, Joan Serra-Sagristà

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

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-oftwo 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 entropycoding parameters. © 2013 SPIE.
Original languageEnglish
Article number12480SS
JournalJournal of Applied Remote Sensing
Volume7
Issue number1
DOIs
Publication statusPublished - 21 Nov 2013

Keywords

  • CCSDS 123.0-B-1
  • Configuration parameters
  • Lossless image coding
  • Multi- and hyperspectral imagery
  • Predictive coding
  • Remote sensing

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