Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane

Research output: Book chapter/Published conference paperConference paper

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Abstract

Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.
Original languageEnglish
Title of host publicationMMM2015
Subtitle of host publication21st proceedings
Place of PublicationSwitzerland
PublisherSpringer
Pages167-179
Number of pages13
ISBN (Electronic)978-3-319-14445-0
ISBN (Print)978-3-319-14444-3
DOIs
Publication statusPublished - 2015
EventInternational Conference on Multimedia Modelling - Sydney, Australia
Duration: 05 Jan 201507 Jan 2015

Conference

ConferenceInternational Conference on Multimedia Modelling
CountryAustralia
Period05/01/1507/01/15

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Redundancy
Wavelength
Pixels

Cite this

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title = "Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane",
abstract = "Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.",
keywords = "And optimal compression plane (OCP), Hyperspectral images, Lossless compression",
author = "Rui Xiao and Manoranjan Paul",
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year = "2015",
doi = "10.1007/978-3-319-14445-0_15",
language = "English",
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booktitle = "MMM2015",
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Xiao, R & Paul, M 2015, Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane. in MMM2015: 21st proceedings. Springer, Switzerland, pp. 167-179, International Conference on Multimedia Modelling, Australia, 05/01/15. https://doi.org/10.1007/978-3-319-14445-0_15

Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane. / Xiao, Rui; Paul, Manoranjan.

MMM2015: 21st proceedings. Switzerland : Springer, 2015. p. 167-179.

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - Efficient Compression of Hyperspectral Images Using Optimal Compression Cube and Image Plane

AU - Xiao, Rui

AU - Paul, Manoranjan

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = Switzerland: Springer, 2015. Event dates (773o) = 5-7 January 2015; Parent title (773t) = International Conference on Multimedia Modelling.

PY - 2015

Y1 - 2015

N2 - Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.

AB - Hyperspectral (HS) images (HSI) provide a vast amount of spatial and spectral information based on the high dimensionality of the pixels in a wide range of wavelengths. A HS image usually requires massive storage capacity, which demands high compression rates to save space with preservation of data integrity. HS image can be deemed as three dimensional data cube where different wavelengths (W) form the third dimension along with X and Y dimensions. To get a better compression result, spatial redundancy of HS images can be exploited using different coders along X, Y, or W direction. This article focuses on taking maximum advantage of HS images redundancy by rearranging HS image into different 3D data cubes and proposes a directionlet based compression scheme constituted the optimal compression plane (OCP) for adaptive best approximation of geometric matrix. The OCP, calculated by the spectral correlation, is used to the prediction and determination of which reconstructed plane can reach higher compression rates while minimizing data loss of hyperspectral data. Moreover, we also rearrange the 3D data cube into different 2D image planes and investigate the compression ratio using different coders. The schema can be used for both lossless and lossy compression. Our experimental results show that the new framework optimizes the performance of the compression using a number of coding methods (inclusive of lossless/lossy HEVC, motion JPEG, JPG2K, and JPEG) for HSIs with different visual content.

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DO - 10.1007/978-3-319-14445-0_15

M3 - Conference paper

SN - 978-3-319-14444-3

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EP - 179

BT - MMM2015

PB - Springer

CY - Switzerland

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