Pre-requisites for smart lossy compression of noisy remote sensing images
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Pre-requisites for smart lossy compression of noisy remote sensing images. / Alhihi, M.; Zemliachenko, A.; Abramov, S.; Vozel, B.; Egiazarian, K.; Lukin, V.
In: Telecommunications and Radio Engineering, Vol. 77, No. 3, 2018, p. 225-241.Research output: Contribution to journal › Article › Scientific › peer-review
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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 -