Using predictive and differential methods with K2-Raster compact data structure for hyperspectral image lossless compression

Kevin Chow*, Dion Eustathios Olivier Tzamarias, Ian Blanes, Joan Serra-Sagristà

*Autor corresponent d’aquest treball

Producció científica: Contribució a revistaArticleRecercaAvaluat per experts

11 Cites (Scopus)

Resum

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k2-raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k2-raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.

Idioma originalAnglès nord-americà
Número d’article2461
RevistaRemote Sensing
Volum11
Número21
DOIs
Estat de la publicacióPublicada - 1 de nov. 2019

Fingerprint

Navegar pels temes de recerca de 'Using predictive and differential methods with K2-Raster compact data structure for hyperspectral image lossless compression'. Junts formen un fingerprint únic.

Com citar-ho