Fast Fractal Image Encoding Algorithm Based on Coefficient of Variation Feature

In order to improve the drawback of fractal image encoding with full search typically requires a very long runtime. This paper thus proposed an effective algorithm to replace algorithm with full search, which is mainly based on newly-defined coefficient of variation feature of image block. During th...

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Bibliographic Details
Published inSmart Graphics Vol. 9317; pp. 175 - 183
Main Authors Li, Gao-ping, Li, Shan-shan
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN9783319538372
3319538373
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-53838-9_15

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Summary:In order to improve the drawback of fractal image encoding with full search typically requires a very long runtime. This paper thus proposed an effective algorithm to replace algorithm with full search, which is mainly based on newly-defined coefficient of variation feature of image block. During the search process, the coefficient of variation feature is utilized to confine efficiently the search space to the vicinity of the domain block having the closest coefficient of variation feature to the input range block being encoded, aiming at reducing the searching scope of similarity matching to accelerate the encoding process. Simulation results of three standard test images show that the proposed scheme averagely obtain the speedup of 4.67 times or so by reducing the searching scope of best-matched block, while can obtain the little lower quality of the decoded images against the full search algorithm. Moreover, it is better than the moment of inertia algorithm.
Bibliography:G. Li—Fund Project: Supported by Application Foundation Projects in Sichuan province (No. 2013JY0180), and Supported by Foundation of Sichuan Educational Committee (No. 15ZA0384), and Supported by Foundation of Southwest University for Nationalities (No. 2012NYT001).
ISBN:9783319538372
3319538373
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-53838-9_15