MSE prediction in DCT-based lossy compression of noise-free and noisy remote sensing

Abstract : Amount of remote sensing data rapidly increases. To transfer, store, and disseminate images, one has to apply compression. Lossless compression often does not satisfy requirements. In turn, lossy compression introduces distortions where their level should be controlled not to lose value of remote sensing data. In this paper, we propose a way to accurately predict mean square error of introduced distortions for DCT-based coder. Peculiarity of our approach is that prediction is considerably faster than compression. This allows estimating the introduced distortion level for a given quantization step and quick adjusting the quantization step. The proposed approach is applicable to both practically noise-free and noisy remote sensing images. © 2018 IEEE.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01808901
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, June 6, 2018 - 11:15:47 AM
Last modification on : Friday, August 9, 2019 - 2:26:02 PM

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S. Krivenko, M. Zriakhov, V. Lukin, B. Vozel. MSE prediction in DCT-based lossy compression of noise-free and noisy remote sensing. 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2018, Feb 2018, Slavske, Ukraine. pp.883-888, ⟨10.1109/TCSET.2018.8336338⟩. ⟨hal-01808901⟩

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