Lossy DCT-based compression of remote sensing images with providing a desired visual quality

Sergey S. Krivenko; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi

doi:10.1117/12.2532726

7 October 2019 Lossy DCT-based compression of remote sensing images with providing a desired visual quality

Sergey S. Krivenko, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, Kacem Chehdi

Proceedings Volume 11155, Image and Signal Processing for Remote Sensing XXV; 1115512 (2019) https://doi.org/10.1117/12.2532726
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

Modern remote sensing (RS) systems produce a huge amount of data that should be passed to potential users from sensors or saved. Then, compression is an operation that is extremely useful where lossy compression has found many applications. A requirement to it is not to loose useful information contained in RS data and to provide a rather high compression ratio (CR). This has to be done in automatic manner and quickly enough. One possible approach to ensure minimal or appropriate loss of useful information is to provide a desired visual quality of compressed images where introduced distortions are invisible. In this paper, we show how this can be done for coders based on discrete cosine transform (DCT) that employ either uniform or non-uniform quantization of DCT coefficients. For multichannel images that contain sub-band images with different dynamic range, it is also proposed to carry out preliminary normalization. Additionally, compression performance can be improved if sub-band images are compressed in groups. Then, either introduced distortions are smaller for a given CR or a larger CR is provided for a given level of compressed data quality. Examples for real-life data are presented.

Citation Download Citation

Sergey S. Krivenko, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, and Kacem Chehdi "Lossy DCT-based compression of remote sensing images with providing a desired visual quality", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115512 (7 October 2019); https://doi.org/10.1117/12.2532726

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