The most important problems in computer applications are the storage and transmission of images that makes the field of developing the image compression methods becomes more important. For that, various compression methods have been proposed using different techniques to achieve high compression ratios and high image quality in low computational time. One of these methods is Fractal image compression, which is a new technique for compressing images. This technique is based on the self-similarities in the images. This work concerned with developing a system model for image compression based on fractals. The developed system consists of two major units; the first is the encoding unit and the second one is the decoding unit. In the encoding unit, the original image is partitioned into range blocks (non overlapping blocks) and domain blocks (possibly overlapping blocks according to a specific step size) using the fixed block size image partitioning scheme. The best matching domain block (i.e. the more self-similar blocks) must be found for each range block by applying an approximate affine transformation. The compression process is finished by storing only the descriptions of these transformations for every range block. The task of finding self-similarities (via the matching process) by a full search of the domain pool is of high computational complexity and is considered to be the major drawback of the fractal image compression method. The decoding unit is typically done by iteratively applying the transformations to any initial image, until convergence is achieved. The decoding module is less computational demanding than the encoding module. The main purpose of this work is to reduce the encoding time of this method. To do so, two approaches are proposed, the first is a new mathematical approach (called Improved Searching Mechanism ISM) that is responsible for determining the IFS-codes between the range and the domain blocks by reduction the total number of the searching operation, while in the second approach (called Loosely Coupled Multiprocessing LCM), the encoding operations is executed using loosely coupled multiprocessing system.