Mr. Alexander N. Zemliachenko Profile

Alexander N. Zemliachenko

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Publications (5)

Proceedings Article | 9 October 2018 Paper

Preliminary filtering and lossy compression of noisy remote sensing images

Alexander Zemliachenko, Sergey Abramov, Vladimir Lukin, Benoit Vozel, Kacem Chehdi

Proceedings Volume 10789, 107890V (2018) https://doi.org/10.1117/12.2325102

KEYWORDS: Image compression, Chromium, Remote sensing, Visualization, Image quality, Image filtering, Transform theory, Optical inspection, Denoising, Image processing

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SPIE Journal Paper | 11 August 2015 Open Access

Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters

Alexander Zemliachenko, Sergey Abramov, Vladimir Lukin, Benoit Vozel, Kacem Chehdi

JARS, Vol. 9, Issue 01, 095066, (August 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.095066

KEYWORDS: Image compression, Remote sensing, Chromium, Visualization, Image quality, Quantization, Interference (communication), Visual compression, Data compression, Hyperspectral imaging

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Proceedings Article | 15 October 2014 Paper

Prediction of optimal operation point existence and parameters in lossy compression of noisy images

Alexander Zemliachenko, Sergey Abramov, Vladimir Lukin, Benoit Vozel, Kacem Chehdi

Proceedings Volume 9244, 92440H (2014) https://doi.org/10.1117/12.2065947

KEYWORDS: Image compression, Visualization, Remote sensing, Chromium, Visual compression, Statistical analysis, Hyperspectral imaging, Denoising, Data compression, Control systems

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SPIE Journal Paper | 11 August 2014 Open Access

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

Alexander Zemliachenko, Ruslan Kozhemiakin, Mikhail Uss, Sergey Abramov, Nikolay Ponomarenko, Vladimir Lukin, Benoît Vozel, Kacem Chehdi

JARS, Vol. 8, Issue 01, 083571, (August 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083571

KEYWORDS: Image compression, Chromium, Hyperspectral imaging, 3D image processing, Data compression, Sensors, Signal to noise ratio, Interference (communication), Image processing, Data acquisition

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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.

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Proceedings Article | 17 October 2013 Paper

VST-based lossy compression of hyperspectral data for new generation sensors

Alexander Zemliachenko, Ruslan Kozhemiakin, Mykhail Uss, Sergey Abramov, Vladimir Lukin, Benoit Vozel, Kacem Chehdi

Proceedings Volume 8892, 88920L (2013) https://doi.org/10.1117/12.2028415

KEYWORDS: Image compression, Chromium, Interference (communication), Data compression, Sensors, Hyperspectral imaging, Data acquisition, Image filtering, Control systems, Remote sensing

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