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Compression Ratio Prediction in Lossy Compression of Noisy Images

Abstract : Our paper addresses a question of prediction compression ratio in lossy compression of remote sensing images by coders based on discrete cosine transform (DCT) taking into account noise present in these images. Quantization step is set fixed and proportional to noise standard deviation to provide compression in optimal operation point if it exists. Simple statistics of DCT coefficients is used for predicting compression ratio. Prediction dependences are obtained off-line (in advance) and they occur to be quite simple and accurate. The influence of DCT statistics on prediction efficiency is analyzed. Accuracy of prediction is studied for real-life hyperspectral data compressed component-wise
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01300968
Contributor : Laurent Jonchère <>
Submitted on : Monday, April 11, 2016 - 3:46:43 PM
Last modification on : Monday, October 5, 2020 - 9:50:23 AM

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  • HAL Id : hal-01300968, version 1

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Alexander N. Zemliachenko, Sergey Abramov, Vladimir V. Lukin, Benoit Vozel, Kacem Chehdi. Compression Ratio Prediction in Lossy Compression of Noisy Images. 2015 IEEE International Geoscience and Remote Sensing Symposium (igarss), Milan, 2015, Milan, Italy. pp.3497--3500. ⟨hal-01300968⟩

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