An Improved Fractal Coding Method based on K-means Clustering
Hui Guo, Jie He · 2016
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 corresponding sub-blocks with father blocks of the same type only within the neighboring scope.This method optimizes 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.