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RESEARCH PRODUCT

An evaluation framework and a benchmark for multi/hyperspectral image compression

Tadeusz SliwaAlamin MansouriYvon VoisinJonathan Delcourt

subject

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image ProcessingComputer sciencebusiness.industryMultispectral image0211 other engineering and technologiesPattern recognition02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingcompressionwaveletsWavelet[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingCompression (functional analysis)Hyperspectral image compression0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessDecorrelation[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMulti/hyperspectral images021101 geological & geomatics engineeringImage compression

description

International audience; 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.

10.4018/ijcvip.2011010105https://hal-univ-bourgogne.archives-ouvertes.fr/hal-00637853/file/IJCVIP.pdf