REVIEW ON OPTIMIZATION TECHNIQUES USED FOR IMAGE COMPRESSION

Abstract

Image compression is most essential requirement for efficient utilization of storage space and transmission bandwidth. Image compression technique involves reducing the size of the image without degrading the quality of the image. Currently many image compression algorithms are used to deal with increasing amount of data involved but still finding the alternative solution is the area of research. This paper reviews some of the Meta heuristic optimization algorithms used for image compression. These algorithms are based on swarm intelligence. Swarm intelligence is a relatively new area that deals with the study of behavior among many entities or objects interacting within the natural or artificial systems. In past few years Swarm Intelligence based algorithms have been applied to a wide variety of problems in combinatorial and continuous optimization, telecommunications, swarm robotics, networking, image processing etc. This paper provides an insight of many optimization techniques used for image compression like Ant Colony Optimization (ACO) algorithm , Harmony Search Algorithm (HSA) and Artificial Bee Colony algorithm, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Ant Colony Optimization algorithm is inspired by the behavior among real ant’s while searching for the food source. Harmony Search Algorithm is inspired by the harmony improvisation process followed while playing music. Particle swarm optimization is an optimization technique inspired by social behavior of bird flocking or fish schooling. Artificial Bee Colony algorithm is motivated by the behavior exhibited by honey bees while searching for the food source. Genetic Algorithm is based on processes observed in the natural evolution.