Lossless Hyperspectral Image Compression Using Binary Tree Based Decomposition

Shampa Shahriyar, Manoranjan Paul, manzur murshed, Mortuza Ali

Research output: Book chapter/Published conference paperConference paper

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

A Hyperspectral (HS) image provides observationalpowers beyond human vision capability but represents more than100 times data compared to a traditional image. To transmit andstore the huge volume of an HS image, we argue that a fundamentalshift is required from the existing 'original pixel intensity'-based coding approaches using traditional image coders (e.g.JPEG) to the 'residual' based approaches using a predictivecoder exploiting band-wise correlation for better compressionperformance. Moreover, as HS images are used in detection orclassification they need to be in original form; lossy schemes cantrim off uninteresting data along with compression, which can beimportant to specific analysis purposes. A modified lossless HScoder is required to exploit spatial-spectral redundancy usingpredictive residual coding. Every spectral band of an HS imagecan be treated like they are the individual frame of a video toimpose inter band prediction. In this paper, we propose a binarytree based lossless predictive HS coding scheme that arrangesthe residual frame into integer residual bitmap. High spatialcorrelation in HS residual frame is exploited by creating largehomogeneous blocks of adaptive size, which are then coded asa unit using context based arithmetic coding. On the standardHS data set, the proposed lossless predictive coding has achievedcompression ratio in the range of 1.92 to 7.94. In this paper, wecompare the proposed method with mainstream lossless coders(JPEG-LS and lossless HEVC). For JPEG-LS, HEVCIntra andHEVCMain, proposed technique has reduced bit-rate by 35%,40% and 6.79% respectively by exploiting spatial correlation inpredicted HS residuals.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-8
Number of pages8
ISBN (Electronic)9781509028962
ISBN (Print)9781509028979
Publication statusPublished - 2016
Event2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Mantra on View Hotel, Surfer's Paradise, Australia
Duration: 30 Nov 201602 Dec 2016
http://dicta2016.dictaconference.org/

Conference

Conference2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CitySurfer's Paradise
Period30/11/1602/12/16
Internet address

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  • Cite this

    Shahriyar, S., Paul, M., murshed, M., & Ali, M. (2016). Lossless Hyperspectral Image Compression Using Binary Tree Based Decomposition. In Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). IEEE, Institute of Electrical and Electronics Engineers.