Low-complexity lossless compression of hyperspectral imagery via linear prediction

Rizzo, F.; Carpentieri, Bruno; Motta, Giovanni; Storer, James A.

doi:10.1109/lsp.2004.840907

Cited by 108 publications

(49 citation statements)

References 6 publications

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“…[16][17][18] When the source A is corrupted by the instrumental noise to yield the observed source, B (5) in which the joint entropy HðA; BÞ is graphically denoted by the union of the spheres HðAÞ and HðBÞ. The addition of noise causes the overall entropy, HðA; BÞ, to increase because the entropy of the noise, HðBjAÞ, also denoted as irrelevance, is added to that of A, i.e., to HðAÞ.…”

Section: Information-theoretic Problem Statementmentioning

confidence: 99%

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Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Aiazzi

Alparone

Baronti

et al. 2013

J. Appl. Remote Sens

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Abstract.A rate-distortion model to measure the impact of near-lossless compression of raw data, that is, compression with user-defined maximum absolute error, on the information available once the compressed data have been received and decompressed is proposed. Such a model requires the original uncompressed raw data and their measured noise variances. Advanced nearlossless methods are exploited only to measure the entropy of the datasets but are not required for on-board compression. In substance, the acquired raw data are regarded as a noisy realization of a noise-free spectral information source. The useful spectral information at the decoder is the mutual information between the unknown ideal source and the decoded source, which is affected by both instrument noise and compression-induced distortion. Experiments on simulated noisy images, in which the noise-free source and the noise realization are exactly known, show the trend of spectral information versus compression distortion, which in turn is related to the coded bit rate or equivalently to the compression ratio through the rate-distortion characteristic of the encoder used on satellite. Preliminary experiments on airborne visible infrared imaging spectrometer (AVIRIS) 2006 Yellowstone sequences match the trends of the simulations. The main conclusion that can be drawn is that the noisier the dataset, the lower the CR that can be tolerated, in order to save a prefixed amount of spectral information. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Information-theoretic Problem Statementmentioning

confidence: 99%

“…A test remote sensing image has been derived from a very high-resolution aerial photograph [pixel spacing 62. 5 Fig. 4.…”

Section: Synthetic Datamentioning

confidence: 99%

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Aiazzi

Alparone

Baronti

et al. 2013

J. Appl. Remote Sens

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“…The prediction estimate uses two pixels in the previous bands in the same spatial position as the current pixel. By comparison, Rizzo et al [5] gave adaptive least-squares optimized prediction technique called spectrum oriented least squares (SLSQ). The prediction coefficients can be computed using an offline procedure on training data.…”

Section: Introductionmentioning

confidence: 99%

Highly Scalable Compression Method for Super Resolution Multi Spectral Images

Raju¹,

Shankar²,

Naidu³

2014

IOSRJVSP

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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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Low-complexity lossless compression of hyperspectral imagery via linear prediction

Cited by 108 publications

References 6 publications

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Highly Scalable Compression Method for Super Resolution Multi Spectral Images

Spectral Distortion in Lossy Compression of Hyperspectral Data

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