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Pre-requisites for smart lossy compression of noisy remote sensing images

Tutkimustuotosvertaisarvioitu

Standard

Pre-requisites for smart lossy compression of noisy remote sensing images. / Alhihi, M.; Zemliachenko, A.; Abramov, S.; Vozel, B.; Egiazarian, K.; Lukin, V.

julkaisussa: Telecommunications and Radio Engineering, Vuosikerta 77, Nro 3, 2018, s. 225-241.

Tutkimustuotosvertaisarvioitu

Harvard

Alhihi, M, Zemliachenko, A, Abramov, S, Vozel, B, Egiazarian, K & Lukin, V 2018, 'Pre-requisites for smart lossy compression of noisy remote sensing images', Telecommunications and Radio Engineering, Vuosikerta. 77, Nro 3, Sivut 225-241. https://doi.org/10.1615/TelecomRadEng.v77.i3.40

APA

Alhihi, M., Zemliachenko, A., Abramov, S., Vozel, B., Egiazarian, K., & Lukin, V. (2018). Pre-requisites for smart lossy compression of noisy remote sensing images. Telecommunications and Radio Engineering, 77(3), 225-241. https://doi.org/10.1615/TelecomRadEng.v77.i3.40

Vancouver

Alhihi M, Zemliachenko A, Abramov S, Vozel B, Egiazarian K, Lukin V. Pre-requisites for smart lossy compression of noisy remote sensing images. Telecommunications and Radio Engineering. 2018;77(3):225-241. https://doi.org/10.1615/TelecomRadEng.v77.i3.40

Author

Alhihi, M. ; Zemliachenko, A. ; Abramov, S. ; Vozel, B. ; Egiazarian, K. ; Lukin, V. / Pre-requisites for smart lossy compression of noisy remote sensing images. Julkaisussa: Telecommunications and Radio Engineering. 2018 ; Vuosikerta 77, Nro 3. Sivut 225-241.

Bibtex - Lataa

@article{4f2fb7cfc9ca42fca8f5f58fea28c8d4,
title = "Pre-requisites for smart lossy compression of noisy remote sensing images",
abstract = "Remote sensing images are usually subject to compression for their further transmission, storage and dissemination. Because of lossy nature of compression, resulting images appear distorted. Degradations of image quality due to compression depend on noisy input image, a type and intensity of noise, and used image coder. To control image degradations, for a given coder, one should predict compression performance to be able to properly choose coder parameter(s). In this paper, we present pre-requisites for such a controlled lossy compression of noisy remote sensing images. The main attention is paid to image coders which are based on discrete cosine transform, due to relatively simple adaptation of its main parameter, quantization step, for controlling the effect of compression.",
keywords = "Image coding, Lossy compression, Noisy image, Remote sensing",
author = "M. Alhihi and A. Zemliachenko and S. Abramov and B. Vozel and K. Egiazarian and V. Lukin",
note = "EXT={"}Lukin, V.{"}",
year = "2018",
doi = "10.1615/TelecomRadEng.v77.i3.40",
language = "English",
volume = "77",
pages = "225--241",
journal = "Telecommunications and Radio Engineering",
issn = "0040-2508",
publisher = "Begell House",
number = "3",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Pre-requisites for smart lossy compression of noisy remote sensing images

AU - Alhihi, M.

AU - Zemliachenko, A.

AU - Abramov, S.

AU - Vozel, B.

AU - Egiazarian, K.

AU - Lukin, V.

N1 - EXT="Lukin, V."

PY - 2018

Y1 - 2018

N2 - Remote sensing images are usually subject to compression for their further transmission, storage and dissemination. Because of lossy nature of compression, resulting images appear distorted. Degradations of image quality due to compression depend on noisy input image, a type and intensity of noise, and used image coder. To control image degradations, for a given coder, one should predict compression performance to be able to properly choose coder parameter(s). In this paper, we present pre-requisites for such a controlled lossy compression of noisy remote sensing images. The main attention is paid to image coders which are based on discrete cosine transform, due to relatively simple adaptation of its main parameter, quantization step, for controlling the effect of compression.

AB - Remote sensing images are usually subject to compression for their further transmission, storage and dissemination. Because of lossy nature of compression, resulting images appear distorted. Degradations of image quality due to compression depend on noisy input image, a type and intensity of noise, and used image coder. To control image degradations, for a given coder, one should predict compression performance to be able to properly choose coder parameter(s). In this paper, we present pre-requisites for such a controlled lossy compression of noisy remote sensing images. The main attention is paid to image coders which are based on discrete cosine transform, due to relatively simple adaptation of its main parameter, quantization step, for controlling the effect of compression.

KW - Image coding

KW - Lossy compression

KW - Noisy image

KW - Remote sensing

U2 - 10.1615/TelecomRadEng.v77.i3.40

DO - 10.1615/TelecomRadEng.v77.i3.40

M3 - Article

VL - 77

SP - 225

EP - 241

JO - Telecommunications and Radio Engineering

JF - Telecommunications and Radio Engineering

SN - 0040-2508

IS - 3

ER -