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Statistical Image Compression using Fast Fourier Coefficients

by M. Kanaka Reddy, V. V. Haragopal, S. A. Jyothi Rani
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 155 - Number 3
Year of Publication: 2016
Authors: M. Kanaka Reddy, V. V. Haragopal, S. A. Jyothi Rani
10.5120/ijca2016912282

M. Kanaka Reddy, V. V. Haragopal, S. A. Jyothi Rani . Statistical Image Compression using Fast Fourier Coefficients. International Journal of Computer Applications. 155, 3 ( Dec 2016), 31-36. DOI=10.5120/ijca2016912282

@article{ 10.5120/ijca2016912282,
author = { M. Kanaka Reddy, V. V. Haragopal, S. A. Jyothi Rani },
title = { Statistical Image Compression using Fast Fourier Coefficients },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 155 },
number = { 3 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume155/number3/26588-2016912282/ },
doi = { 10.5120/ijca2016912282 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:00:19.200152+05:30
%A M. Kanaka Reddy
%A V. V. Haragopal
%A S. A. Jyothi Rani
%T Statistical Image Compression using Fast Fourier Coefficients
%J International Journal of Computer Applications
%@ 0975-8887
%V 155
%N 3
%P 31-36
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Uncompressed images occupy more memory and it contains redundant data. For storage and transmission efficiency compression is required. The purpose of image compression is to reduce the number of bits to represent in image while maintaining visual quality of images. In this paper we implement the Fast Fourier co efficient Transform with non overlapping 3 x 3, 9 x 9 and 27x 27 block sizes of the images. The purpose of the study is to reduce the original image into asmall set of pixels by using the Fourier coefficients with this idea the compression is successfully implemented on various images, computed the Peak Signal Noise Ratio (PSNR) and Compression Ratio (CR) for the various images.

References
  1. Ghafourian, M., and Huang.1995 Comparison between several adaptive search vector quantization schemes and JPEG standard for image compression, IEEE
  2. Donoho, D. L., and I. Johnstone, M. 1998 Minimax estimation viawavelet shrinkage, Annals of Statistics 26, 879–921.
  3. Shapiro J. 1993 Embedded image coding using zero trees of wavele Coefficients, IEEE Trans. on Signal Processing, pp: 3445-3462
  4. Rafael C Gonzalez, Richard E Woods (2008), “Digital Image Processing” Low price edition, Pearson education, ISBN – 978-81-317-1934-3.
  5. Chanda, Majumder Dutta B (2004),“Digital Image Processing and Analysis” Prentice –Hall of India.
  6. Padmaja.N (2012), “A statistical Approach to Feature Extraction & Image Compression”, Ph.D thesis.
Index Terms

Computer Science
Information Sciences

Keywords

Fast Fourier Transform (FFT) Root Mean Square Error (RMSE) Compression Ratio (CR) Peak Signal Noise Ratio (PSNR) Image.

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