Massively parallel processing approach to fractal image compression with near-optimal coefficient quantization

In recent years Image Fractal Compression techniques (IFS) have gained more interest because of their capability to achieve high compression ratios while maintaining very good quality of the reconstructed image. The main drawback of such techniques is the very high computing time needed to determine the compressed code. In this work, after a brief description of IFS theory, we introduce the coefficient quantization problem, presenting two algorithms for its solution: the first one is based on Simulated Annealing while the second refers to a fast iterative algorithm. We discuss IFS parallel implementation at different level of granularity and we show that Massively Parallel Processing on SIMD machines is the best way to use all the large granularity parallelism offered by the problem. The results we present are achieved implementing the proposed algorithms for IFS compression and coefficient quantization on the MPP APE100/Quadrics machine.