A Fast Fractal Image Compression Algorithm Based on Average-Variance Function

ChenGuang ZHOU; Kui MENG; ZuLian QIU

doi:10.1093/ietisy/e89-d.3.1303

A Fast Fractal Image Compression Algorithm Based on Average-Variance Function

ChenGuang ZHOU, Kui MENG, ZuLian QIU

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In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30-40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.3 pp.1303-1308

Publication Date: 2006/03/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.3.1303

Type of Manuscript: LETTER

Category: Image Processing and Video Processing

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ChenGuang ZHOU, Kui MENG, ZuLian QIU, "A Fast Fractal Image Compression Algorithm Based on Average-Variance Function" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 3, pp. 1303-1308, March 2006, doi: 10.1093/ietisy/e89-d.3.1303.
Abstract: In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30-40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.3.1303/_p

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@ARTICLE{e89-d_3_1303,
author={ChenGuang ZHOU, Kui MENG, ZuLian QIU, },
journal={IEICE TRANSACTIONS on Information},
title={A Fast Fractal Image Compression Algorithm Based on Average-Variance Function},
year={2006},
volume={E89-D},
number={3},
pages={1303-1308},
abstract={In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30-40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.},
keywords={},
doi={10.1093/ietisy/e89-d.3.1303},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - A Fast Fractal Image Compression Algorithm Based on Average-Variance Function
T2 - IEICE TRANSACTIONS on Information
SP - 1303
EP - 1308
AU - ChenGuang ZHOU
AU - Kui MENG
AU - ZuLian QIU
PY - 2006
DO - 10.1093/ietisy/e89-d.3.1303
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2006
AB - In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30-40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.
ER -