A Hierarchical Classification Matching Scheme for Fractal Image Compression

A hierarchical classification matching scheme for fractal image compression is proposed in this paper. This scheme reduces the encoding time dramatically by partitioning the domain pools hierarchically. Experimental results on standard gray scale image show that the hierarchical classification matching scheme yields much better performance over other classification matching scheme. Compared with Fisherpsilas quadtree method, the encoding time of the proposed algorithm for Lena reduces by 84.07% in average, while the compression ratio increases by 14.56% and the PSNR of the reconstructed image is also increased by 3.23%.