A SURVEY ON PARALLEL COMPUTING OF IMAGE COMPRESSION ALGORITHMS JPEG and Fractal Image Compression

This paper presents a short survey on the parallel computing of JPEG and Fractal image compression algorithms. Image compression is a type of data compression. Data Compression generally involves encoding techniques that uses fewer bits than the original representation. Image compression uses various techniques that will remove the redundant and the irrelevant information from the image. Image compression can thus efficiently reduce the storage space required and also speed up the transmission. However, most of the image compression techniques have problems like computational complexity, load etc. Parallel computing can effectively improve the processing speed. JPEG and fractal image compressions are two of the efficient techniques available in image compression. With the availability of the high performance computing in the form of multicore processing systems and GPUs can greatly accelerate the processing of the JPEG image compression technique. Fractal image compression takes advantage of the natural affine redundancy present in the typical images to achieve a high compression ratio. To speed up the compression process the sequential fractal image compression algorithm needs to be converted into parallel fractal image compression algorithm, this translation exploits the inherently parallel nature