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Satellite Image Compression using Fractional Fourier Transform

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 3
Year of Publication: 2012
Authors:
Rajinder Kumar
Kulbir Singh
Rajesh Khanna
10.5120/7752-0810

Rajinder Kumar, Kulbir Singh and Rajesh Khanna. Article: Satellite Image Compression using Fractional Fourier Transform. International Journal of Computer Applications 50(3):20-25, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Rajinder Kumar and Kulbir Singh and Rajesh Khanna},
	title = {Article: Satellite Image Compression using Fractional Fourier Transform},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {3},
	pages = {20-25},
	month = {July},
	note = {Full text available}
}

Abstract

The Fourier transform can be successfully used in the field of signal processing, image processing, communications and data compression applications. The discrete fractional Fourier transform, generalization of the discrete Fourier transform, is used for compression of high resolution satellite images. With the extra degree of freedom provided by the DFrFT, its fractional order 'a', high visual quality decompressed image can be achieved. Different satellite images of size 512×512 and 256×256 are studied and performance parameters such as peak signal-to-noise ratio (PSNR), mean square error (MSE) and compression ratio (CR) are determined. After subdivide the images, DFrFt is applied to obtain the transformed coefficients for calculating PSNR and IDFrFt is applied for reconstruction of satellite images. It is analyzed that by changing the value of fractional order 'a' to different value, the DFrFT can achieved minimum MSE and corresponding maximum PSNR between 0. 8 to 1 fractional order for same amount of CR. It is observed that discrete fractional Fourier transform is very efficient for obtaining better PSNR around 41 dB at 50% CR while maintaining the higher visual quality of decompressed satellite images. The significant improvement is observed using DFrFT as compare to existing classical lifting scheme for satellite image compression based on discrete wavelet transform (DWT).

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