Share Email Print

Journal of Applied Remote Sensing • Open Access

Lossy compression of noisy remote sensing images with prediction of optimal operation point existence and parameters
Author(s): Alexander N. Zemliachenko; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi

Paper Abstract

We address lossy compression of noisy remote sensing images, where the noise is supposed to be spatially uncorrelated (white), additive originally or after a proper variance-stabilizing transformation (VST). In such situations, the so-called optimal operation point (OOP) might exist. The OOP is associated with the parameter that controls compression (e.g., quantization step) for which the compressed image is the closest to the noise-free image according to a certain criterion and is closer than the original noisy image. Lossy compression in the neighborhood of OOP, if it exists, relates to an essential noise filtering effect and some distortions. Then such lossy compression (in the neighborhood of OOP) becomes expedient. However, it may be that OOP does not exist for a given image and the observed noise intensity. In such a situation, it can be reasonable to carry out a more “careful” image compression (with a lower compression ratio). Also, it is expedient to predict the existence of OOP and the compression parameters at this point in advance in order to perform adaptive and automated compression. The OOP existence that can be predicted for some coders based on the discrete cosine transform (DCT) is shown. The proposed prediction procedure is simple and fast. It presumes the calculation of DCT coefficient statistics in nonoverlapping 8×8  pixel blocks for a given image and uses an approximating curve obtained in advance. It is shown that it is possible to predict values for both conventional metrics, such as mean square error or peak-signal-to-noise ratio, and some visual quality metrics for the coder parameters that correspond to a possible OOP. The designed prediction procedure is tested on Hyperion and AVIRIS hyperspectral remote sensing data.

Paper Details

Date Published: 11 August 2015
PDF: 26 pages
J. Appl. Rem. Sens. 9(1) 095066 doi: 10.1117/1.JRS.9.095066
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Alexander N. Zemliachenko, National Aerospace Univ. (Ukraine)
Sergey K. Abramov, National Aerospace Univ. (Ukraine)
Vladimir V. Lukin, National Aerospace Univ. (Ukraine)
Benoit Vozel, Univ. de Rennes 1 (France)
Kacem Chehdi, Univ. de Rennes 1 (France)

© SPIE. Terms of Use
Back to Top