International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 525 Fractal Image Compression By Range Block Classification Miss. Gauri R. Desai 1 , Dr. Mahesh S. Chavan 2 1 PG Student, Department of Electronics Engineering KIT’s COE Kolhapur, Maharashtra, India 2 Professor, Department of Electronics Engineering KIT’s COE Kolhapur, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Image compression is a technique in which we can store the huge amount of images, videos in less memory. Which will helpful to increase storage capacity and transmission performance, For the Fractal image compression lossy compression is used. Mainly the fractal image compression involves partitioning the images into Range Blocks and Domain blocks. Then each range block searches for best domain block by using particle swarm optimization Algorithm. Key Words: Fitness function, Fractal block coding, Image data compression, Particle swarm optimization, reduced domain block. 1. INTRODUCTION Mainly there are two types of compression techniques namely lossy and lossless data compression. Here in fractal image compression lossy technique is used it gives the constructed image is actually an approximation of input image that is original image. Fractal image code is implemented by Barnsley and Jacquin. The main advantage of fractal image compression it gives high data compression ratio, and less decompression time. But the main disadvantage with this technique is large encoding time for image data compression. At present in this paper we have focused on enhancing the data compression ratio and improves the image quality after the decompression. Fractal means the geometrical figure obtained by partitioning the original image into range blocks and domain blocks then each range block finds the best matching domain block iteratively by using particle swarm optimization algorithm. Particle swarm optimization algorithm is mainly population based algorithm. Introduced by Kennedy & Eberhart in 1995. Inspired by social behavior of birds and fish. All the particles searches for the best result. If one of the particle finds the best results then remaining all will follow the same. Every particle has own memory, it searches for best matched range block with domain block iteratively by self-similar property. 2. PARTICLE SWARM OPTIMIZATION ALGORITHM Particle swarm optimization algorithm is population based algorithm introduced by Kennedy and Eberhart in 1995. PSO idea emerged from group of birds, schools of fish, or swarm of bees. As it is population based method solves various function optimization problems. When the swarm of birds searches for food in different places ,if anyone has found the food then remaining all will follow to that bird for food this idea is implemented for particle swarm optimization here swarm of birds means the swarm of particles, each particle has its own position and velocity. Individual particle searches for best optimization solution that is called position best solution (pbest). Again the particle update its position and velocity for best results. Particle every time update its position and velocity iteratively and final optimization result called as Gbest. Fig- 1: PSO Algorithm