Analyzing Performance of the Parallel-based Fractal Image Compression Problem on Multicore Systems

Both the size and the resolution of images always were key topics in the graphical computing area. Especially, they become more and more relevant in the big data era. We can observe that often a huge amount of data is exchanged by medium/low bandwidth networks or yet, they need to be stored on devices with limited space of memory. In this context, the present paper shows the use of the Fractal method for image compression. It is a lossy method known by providing higher indexes of file reduction through a highly time consuming phase. In this way, we developed a model of parallel application for exploiting the power of multiprocessor architectures in order to get the Fractal method advantages in a feasible time. The evaluation was done with different-sized images as well as by using two types of machines, one with two and another with four cores. The results demonstrated that both the speedup and efficiency are highly dependent of the number of cores. They emphasized that a large number of threads does not always represent a better performance.