Optimized Onboard Lossless and Near-Lossless Compression of Hyperspectral Data Using CALIC

Magli, Enrico; Olmo, Gabriella; Quacchio, E.

doi:10.1109/lgrs.2003.822312

Cited by 179 publications

(105 citation statements)

References 8 publications

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“…lossless (Magli, Olmo, and Quacchio 2004) and lossy compression (Du, Zhu, and Fowler 2008;Penna et al 2007;Wang, Rucker, and Fowler 2004). For lossless compression, images are encoded without loss of information thus the original images can be fully recovered when decoded.…”

Section: Introductionmentioning

confidence: 99%

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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This version is available at https://strathprints.strath.ac.uk/50084/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Effective compression of hyperspectral imagery using an improved 3DDCT approach for land cover analysis in remote sensing applications Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applicationsAlthough hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI,where the accuracy of analysis from decoded data is taken as a key criterion to

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Section: Introductionmentioning

confidence: 99%

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao

Ren

Sun

et al. 2014

International Journal of Remote Sensing

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“…The latter indicates that the decompressed data have a user-defined maximum absolute error, being zero in the lossless case. Several variants exist in prediction schemes, the most performing being adaptive [2][3][4][5][6]. Lossless compression thoroughly preserves the information of the data but allows a moderate decrement in transmission bit rate to be achieved.…”

Section: Introductionmentioning

confidence: 99%

Spectral Distortion in Lossy Compression of Hyperspectral Data

Aiazzi

Alparone

Baronti

et al. 2012

Journal of Electrical and Computer Engineering

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Distortion allocation varying with wavelength in lossy compression of hyperspectral imagery is investigated, with the aim of minimizing the spectral distortion between original and decompressed data. The absolute angular error, or spectral angle mapper (SAM), is used to quantify spectral distortion, while radiometric distortions are measured by maximum absolute deviation (MAD) for near-lossless methods, for example, differential pulse code modulation (DPCM), or mean-squared error (MSE) for lossy methods, for example, spectral decorrelation followed by JPEG 2000. Two strategies of interband distortion allocation are compared: given a target average bit rate, distortion may be set to be constant with wavelength. Otherwise, it may be allocated proportionally to the noise level of each band, according to the virtually lossless protocol. Comparisons with the uncompressed originals show that the average SAM of radiance spectra is minimized by constant distortion allocation to radiance data. However, variable distortion allocation according to the virtually lossless protocol yields significantly lower SAM in case of reflectance spectra obtained from compressed radiance data, if compared with the constant distortion allocation at the same compression ratio.

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“…Magli et al [16] extend this work by noting that the 3D CALIC proposed in [15] does not make full use of the spectral resolution available in hyperspectral images. They show that the performance of 3-D CALIC can be significantly improved by taking account of the fact that in hyperspectral images spectral prediction can be enhanced by taking into account multiple bands.…”

Section: Interband Calicmentioning

confidence: 94%

“…Two dimensional CALIC, was extended to 3-dimensional hyperspectral images by Wu and Memon [15]. The three dimensional CALIC was then improved upon by Magli et al [16]. We present how the prediction in CALIC is extended from 2-D to 3-D and how spectral correlation is used to improve the prediction.…”

Section: Interband Context-based Adaptive Lossless Image Codec (Calic)mentioning

confidence: 99%

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Lossless Predictive Compression of Hyperspectral Images

Wang

Sayood

Hyperspectral Data Compression

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Optimized Onboard Lossless and Near-Lossless Compression of Hyperspectral Data Using CALIC

Cited by 179 publications

References 8 publications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Spectral Distortion in Lossy Compression of Hyperspectral Data

Lossless Predictive Compression of Hyperspectral Images

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