The Use of Quadtree Range Domain Partitioning with Fast Double Moment Descriptors to Enhance FIC of Colored Image

DOI: https://doi.org/10.14500/aro.10207
Keywords: Fractal image compression, Iterated function system, Moments features, Quadtree

Abstract

In this paper, an enhanced fractal image compression system (FIC) is proposed; it is based on using both symmetry prediction and blocks indexing to speed up the blocks matching process. The proposed FIC uses quad tree as variable range block partitioning mechanism. two criteria’s for guiding the partitioning decision are used: The first one uses sobel-based edge magnitude, whereas the second uses the contrast of block. A new set of moment descriptors are introduced, they differ from the previously used descriptors by their ability to emphasize the weights of different parts of each block. The effectiveness of all possible combinations of double moments descriptors has been investigated. Furthermore, a fast computation mechanism is introduced to compute the moments attended to improve the overall computation cost. the results of applied tests on the system for the cases “variable and fixed range” block partitioning mechanism indicated that the variable partitioning scheme can produce better results than fixed partitioning one (that is, 4 × 4 block) in term of compression ratio, faster than and PSNR does not significantly decreased.

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Author Biographies

Bushra A. Sultan, Department of Computer Science, University of Baghdad, College of Science, Baghdad
Bushra A. Sultan has a Ph.D. degree in computer science from University of Technology. Her main research interests are in multimedia and compression. Currently, she is working as a lecturer in Department of Computer Science, College of Science, University of Baghdad.
Loay E. George, Department of Computer Science, University of Baghdad, College of Science, Baghdad
He has a Ph.D. degree in Physics/Digital Image Processing from University of Baghdad-College of Science. His main research interests are in multimedia, compression, artificial intelligence. Currently, he is working as Assistant Professor at the same University.
Nidaa F. Hassan, Department of Computer Science, University of Technology , Baghdad
Nidaa F. Hassan is an Asistant Professor. She received the M.Sc. and Ph.D. degrees in Computer Science from University of Technology, Baghdad, Iraq, in 1996 and 2005, respectively. She has around 21 years of teaching experience. Her areas of  interests are computer security and image processing.

References

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ARO Journal: Volume 6, No. 1 (2018)
Published
2018-03-24
How to Cite
Sultan, B. A., George, L. E. and Hassan, N. F. (2018) “The Use of Quadtree Range Domain Partitioning with Fast Double Moment Descriptors to Enhance FIC of Colored Image”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 6(1), pp. 13-22. doi: 10.14500/aro.10207.
Issue
Vol. 6 No. 1 (2018): Issue Ten
Section
Articles

Copyright (c) 2018 Bushra A. Sultan, Loay E. George, Nidaa F. Hassan

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