A Comparative Study on Nature Inspired Optimization Algorithms in Fractal Image Compression

  • Kanimozhi Rajasekaran, P.D. Sathya

Abstract

Image compression plays a crucial role in the world of digital, because of its ability to avoid repetition and save the storage space for processing of more number of images. In various fields, more amount of space is required for storing of datas or images without reducing its quality for future reference. Thus, making image compression technique mandatory for storing and transmission of digital images. Recently, Fractal image compression (FIC) technique is used for compression of images. Fractal image compression suffers a major drawback which is shortage of speed, it takes more time for compressing the images compared to decompressing the images. FIC is a lossy compression technique. To overcome this, variety of optimization techniques with different capabilities such as Particle Swarm Optimization (PSO), Flower Pollination algorithm(FPA) has been used in order to improve the efficiency of fractal image compression. Each and every algorithm has its own speciality. PSO algorithm reduces the encoding time by maintaining the quality of the image. However, PSO is weak in local search ability to find optimal solution. So, to find better optimal solution and for better visual quality of image, Flower Pollination algorithm (FPA) is used. FPA enhances the performance in local and global searches. In this paper, FPA based FIC is compared with PSO based FIC. To maintain the quality of retrieved images and to increase the speed of encoding, FPA proves better with good compression ratio and PSNR value.

Published
2017-12-31
Section
Articles