Faster fractal image compression using quadtree recomposition

In this paper, we present a fractal image compression algorithm employing a new quadtree recomposition (QR) approach. In this approach, quadtree subblocks of an image are iteratively recombined into larger blocks for fractal coding. For complex images, this approach exhibits superior runtime performance, when compared to a classical quadtree decomposition (QD) scheme, while maintaining high delity for reconstructed images. Quantitative results include an evaluation of attained compression ratios, runtime performance, and signal-to-noise ratios (SNR) for reconstructed images.