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Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Tutkimustuotosvertaisarvioitu

Standard

Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform. / Zemliachenko, Alexander N.; Kozhemiakin, Ruslan A.; Uss, Mikhail L.; Abramov, Sergey K.; Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Vozel, Benoît; Chehdi, Kacem.

julkaisussa: Journal Of Applied Remote Sensing, Vuosikerta 8, Nro 1, 083571, 2014.

Tutkimustuotosvertaisarvioitu

Harvard

Zemliachenko, AN, Kozhemiakin, RA, Uss, ML, Abramov, SK, Ponomarenko, NN, Lukin, VV, Vozel, B & Chehdi, K 2014, 'Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform', Journal Of Applied Remote Sensing, Vuosikerta. 8, Nro 1, 083571. https://doi.org/10.1117/1.JRS.8.083571

APA

Zemliachenko, A. N., Kozhemiakin, R. A., Uss, M. L., Abramov, S. K., Ponomarenko, N. N., Lukin, V. V., ... Chehdi, K. (2014). Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform. Journal Of Applied Remote Sensing, 8(1), [083571]. https://doi.org/10.1117/1.JRS.8.083571

Vancouver

Zemliachenko AN, Kozhemiakin RA, Uss ML, Abramov SK, Ponomarenko NN, Lukin VV et al. Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform. Journal Of Applied Remote Sensing. 2014;8(1). 083571. https://doi.org/10.1117/1.JRS.8.083571

Author

Zemliachenko, Alexander N. ; Kozhemiakin, Ruslan A. ; Uss, Mikhail L. ; Abramov, Sergey K. ; Ponomarenko, Nikolay N. ; Lukin, Vladimir V. ; Vozel, Benoît ; Chehdi, Kacem. / Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform. Julkaisussa: Journal Of Applied Remote Sensing. 2014 ; Vuosikerta 8, Nro 1.

Bibtex - Lataa

@article{a949f7fbd04b415eb4f783ea504833c4,
title = "Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform",
abstract = "A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and {"}difference{"} images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.",
keywords = "3-dimensional coders, hyperspectral data, lossy compression, optimal operation point, variance stabilizing transform",
author = "Zemliachenko, {Alexander N.} and Kozhemiakin, {Ruslan A.} and Uss, {Mikhail L.} and Abramov, {Sergey K.} and Ponomarenko, {Nikolay N.} and Lukin, {Vladimir V.} and Beno{\^i}t Vozel and Kacem Chehdi",
year = "2014",
doi = "10.1117/1.JRS.8.083571",
language = "English",
volume = "8",
journal = "Journal Of Applied Remote Sensing",
issn = "1931-3195",
publisher = "Society of Photo-optical Instrumentation Engineers",
number = "1",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

AU - Zemliachenko, Alexander N.

AU - Kozhemiakin, Ruslan A.

AU - Uss, Mikhail L.

AU - Abramov, Sergey K.

AU - Ponomarenko, Nikolay N.

AU - Lukin, Vladimir V.

AU - Vozel, Benoît

AU - Chehdi, Kacem

PY - 2014

Y1 - 2014

N2 - A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

AB - A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

KW - 3-dimensional coders

KW - hyperspectral data

KW - lossy compression

KW - optimal operation point

KW - variance stabilizing transform

UR - http://www.scopus.com/inward/record.url?scp=84922978778&partnerID=8YFLogxK

U2 - 10.1117/1.JRS.8.083571

DO - 10.1117/1.JRS.8.083571

M3 - Article

VL - 8

JO - Journal Of Applied Remote Sensing

JF - Journal Of Applied Remote Sensing

SN - 1931-3195

IS - 1

M1 - 083571

ER -