Distributed fractal image compression on PVM for million-pixel images

Abstract

We propose the distributed fractal image compression and decompression on a parallel virtual machine (PVM) system. We apply a regional search for the fractal image compression to reduce the communication cost on the distributed system PVM. The regional search is a partitioned iterated function system search from a region of the image instead of over the whole image. Because the area surrounding a partitioned block is similar to this block possibly, finding the fractal codes by regional search has a higher compression ratio and less compression time. When implemented on the PVM, the fractal image compression using regional search reduces the compression time with lower compression loss. When we compress the image Lena with an image size of 1024/spl times/1024 using a region size of 512/spl times/512 on the PVM with 4 Pentium II-300 PCs, the compression time is 13.6 seconds, the compression ratio is 6.34 and the PSNR is 38.59. However, it takes 176 seconds, have a compression ratio of 6.30 and have a PSNR of 39.68 by the conventional fractal image compression. In addition, when the region size is 128/spl times/128, the compression time is 7.8 seconds, the compression ratio is 7.53 and the PSNR is 36.67. In the future, we can apply this method to the fractal image compression using neural networks.