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Fractal based Image Compression Techniques

by Sandhya Kadam, Vijay Rathod
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 1
Year of Publication: 2017
Authors: Sandhya Kadam, Vijay Rathod
10.5120/ijca2017915711

Sandhya Kadam, Vijay Rathod . Fractal based Image Compression Techniques. International Journal of Computer Applications. 178, 1 ( Nov 2017), 11-18. DOI=10.5120/ijca2017915711

@article{ 10.5120/ijca2017915711,
author = { Sandhya Kadam, Vijay Rathod },
title = { Fractal based Image Compression Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 178 },
number = { 1 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number1/28637-2017915711/ },
doi = { 10.5120/ijca2017915711 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:49:12.586075+05:30
%A Sandhya Kadam
%A Vijay Rathod
%T Fractal based Image Compression Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 1
%P 11-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fractal image compression offers high compression ratios and quality image reconstruction. It uses various techniques as the fractal with DCT, wavelet, neural network, genetic algorithms, quantum acceleration etc. Additionally, because fractals are infinitely magnifiable, fractal compression is resolution independent and so a single compressed image can be used efficiently for display in any image resolution including resolution higher than the resolution of the original image. Breaking an image into pieces and identifying self-similar ones is the main principle of the approach. In this paper, the different issues in fractal image compression as partitioning, larger encoding time, compression ratio, quality of the reconstructed image, decoding time, SSIM(Structured Similarity Index) are discussed and highlighted. The various areas for improvement as larger encoding time and PSNR are also suggested. The various parameters for evaluating the performance of these techniques as PSNR, compression ratio, encoding time, and decoding time are also suggested. Comparison of Fractal techniques for color image, texture and satellite image is done using different parameters as compression time, compression ratio and PSNR. The hybrid method which combines Fractal quad tree with wavelet and Huffman coding is implemented and compared different parameters as compression ratio and the compression time of the proposed method with the existing methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform Fractal Image Compression Partitioning Affine Transformations PSNR Quad tree Self Similarity Wavelet Transform.

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