Near-lossless compression of hyperspectral images

Agnieszka Miguel, Jenny Liu, Dane Barney, Richard Ladner, Eve Riskin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Algorithms for near-lossless compression of hyperspectral images are presented. They guarantee that the intensity of any pixel in the decompressed image(s) differs from its original value by no more than a user-specified quantity. To reduce the bit rate required to code images while providing significantly more compression than lossless algorithms, linear prediction between the bands is used. Each band is predicted by a previously transmitted band. The prediction is subtracted from the original band, and the residual is compressed with a bit plane coder which uses context-based adaptive binary arithmetic coding. To find the best prediction algorithm, the impact of various band orderings and optimization techniques on the compression ratios is studied.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages1153-1156
Number of pages4
DOIs
StatePublished - 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: 8 Oct 200611 Oct 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2006 IEEE International Conference on Image Processing, ICIP 2006
Country/TerritoryUnited States
CityAtlanta, GA
Period8/10/0611/10/06

Keywords

  • Data compression
  • Distortion
  • Image coding
  • Image storage
  • Remote sensing

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