Treffer: Low-complexity and error-resilient hyperspectral image compression based on distributed source coding

Title:
Low-complexity and error-resilient hyperspectral image compression based on distributed source coding
Authors:
ABRARDO A., BARNI M., BERTOLI A., GARZELLI A., MAGLI E., NENCINI F., PENNA B., VITULLI R.
Source:
SPIE Proceedings. 7109:71090V
Publisher Information:
SPIE, 2008.
Publication Year:
2008
Subject Terms:
0203 mechanical engineering, 0202 electrical engineering, electronic engineering, information engineering, Distributed source coding, Error resilience, Hyperspectral compression, Lossless compression, 02 engineering and technology
Document Type:
Fachzeitschrift Article<br />Other literature type<br />Conference object
File Description:
application/pdf
ISSN:
0277-786X
DOI:
10.1117/12.799990
Access URL:
https://iris.polito.it/retrieve/handle/11583/1895982/436651/europto_08.pdf
https://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=796316
https://iris.polito.it/handle/11583/1895982
https://ui.adsabs.harvard.edu/abs/2008SPIE.7109E..0VA/abstract
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/7109/1/Low-complexity-and-error-resilient-hyperspectral-image-compression-based-on/10.1117/12.799990.full
https://core.ac.uk/display/11405893
https://hdl.handle.net/11365/3319
https://doi.org/10.1117/12.799990
https://hdl.handle.net/11583/1895982
Accession Number:
edsair.doi.dedup.....7e06ed5c95f59b32def6ac03f76749ee
Database:
OpenAIRE

Weitere Informationen

In this paper we propose a lossless compression algorithm for hyperspectral images based on distributed source coding; this algorithm represents a significant improvement over our prior work on the same topic, and has been developed during a project funded by ESA-ESTEC. In particular, the algorithm achieves good compression performance with very low complexity; moreover, it also features a very good degree of error resilience. These features are obtained taking inspiration from distributed source coding, and particularly employing coset codes and CRC-based decoding. As the CRC can be used to decode blocks using a reference different from that used to compress the image, this yields error resilience. In particular, if a block is lost, decoding using the closest collocated block in the second previous band is successful about 70% of the times.