An Improved Fractal Coding Method based on K-means Clustering

DOI

10.2991/mmme-16.2016.67How to use a DOI?

Keywords

Fractal image coding; k-means clustering; nearest neighbor search; variance method

Abstract

This paper focuses on a fast fractal coding algorithm based on k-means clustering. First of all, the variance method is employed to divide the sub-blocks into simple sub-blocks and complex sub-blocks; then, the k-means clustering algorithm is applied to classify the complex sub-blocks and father blocks, and the approach of nearest neighbor search is applied in the process of searching for matching father blocks, so as to match cor-responding sub-blocks with father blocks of the same type only within the neighboring scope. This method op-timizes the process of searching for matching blocks, thereby greatly shortening the encoding duration. Test results show that compared with the basic fractal coding algorithm, this method can increase the encoding speed by about 570 times, and lead to high quality of the reconstructed image.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hui Guo
AU  - Jie He
PY  - 2016/10
DA  - 2016/10
TI  - An Improved Fractal Coding Method based on K-means Clustering
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 294
EP  - 300
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.67
DO  - 10.2991/mmme-16.2016.67
ID  - Guo2016/10
ER  -