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Achieving high data compression of self-similar satellite imagesusing fractal

Woon, Wee Meng, Ho, Anthony T. S., Yu, Tao, Tam, Siu Chung, Tan, Siong Chai and Yap, Lian Teck (2000) Achieving high data compression of self-similar satellite imagesusing fractal In: IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

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Abstract

The authors examine and describe an implementation of a fractal compression method on optical satellite images. The basic principle is that an image can be reconstructed by using the self similarities in the image itself. The satellite image is first partitioned into a set of non-overlapping ranges. For each range, a “best matching” domain block would be found and a set of affine transformation would be performed. The compression would be obtained by storing only the descriptions of this transformation. The fractal compression method is implemented on two images, a complicated city image and a simple coastal area image. Using a fractal algorithm, the data integrity of the coastal area image was maintained with a peak signal-to-noise ratio (PSNR) of approximately 34.9 dB while achieving a compression ratio of 172:1. A novel approach of using a combination of fractal and wavelet algorithms for data compression is also described.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: In Proceedings of the IEEE 2000 International Geoscience and Remote Sensing Symposium, IGARSS 2000, Volume 2, pp. 609-611.© 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This article was published when Anthony T. S. Ho was at Nanyang Technological University, Singapore.
Divisions: Faculty of Engineering and Physical Sciences > Computing Science
Depositing User: Mr Adam Field
Date Deposited: 27 May 2010 14:45
Last Modified: 23 Sep 2013 18:35
URI: http://epubs.surrey.ac.uk/id/eprint/1907

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