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An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression

An Evaluation Framework and a Benchmark for Multi/Hyperspectral Image Compression
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Author(s): Jonathan Delcourt (University of Burgundy, France), Alamin Mansouri (University of Burgundy, France), Tadeusz Sliwa (University of Burgundy, France)and Yvon Voisin (University of Burgundy, France)
Copyright: 2013
Pages: 14
Source title: Image Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-3994-2.ch015

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Abstract

This paper benchmarks three multi/hyperspectral image compression approaches: the classic Multi-2D compression approach and two different implementations of 3D approach (Full 3D and Hybrid). All approaches are combined with a spectral PCA decorrelation stage to optimize performance. These three compression approaches are compared within a larger comparison framework than the conventionally used PSNR, which includes eight metrics divided into three families. The comparison is carried out with regard to variations in bitrates, spatial, and spectral dimensions variations of images. The time and memory consumption difference between the three approaches is also discussed. Results of this comparison show the weaknesses and strengths of each approach.

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