Hybrid Lifting Based Image Compression Scheme Using Particle Swarm Optimization Algorithm and Artifical Bee Colony Algorithm

Abstract: Wavelet transform is one of the compression techniques used for images and multimedia files. This paper proposes a framework for constructing adaptive and hybrid lifting scheme with particle swarm optimization algorithm and Artificial bee colony algorithm. .Particle swarm optimization algorithm is used to find the prediction function P in the lifting scheme and Artificial bee colony algorithm is used to find different update coefficient by local search, atlast optimally choose the best update coefficient to get best quality of compressed image. Particle swarm Optimization technique is used to improve the accuracy of the prediction function used in the lifting scheme where as in the update step we modifying the center pixels with the co-efficient in 8 – different direction with a considerable window size by using local search , we obtain a best directional window with a considerable size using artificial bee colony algorithm to obtain the best reconstructed image, which can be considered as an optimization task. Artificial Bee Colony algorithm which is a recent and successful optimization tool is used to determine the directional window size to produce the best compressed image in terms of both compression ratio and quality.The proposed hybrid lifting scheme is very much useful for image compression compared to the wavelet transforms The proposed work gives better PSNR when compared to existing methods. So, the transmission cost to transmit the encoded data and memory space to store the encoded data can be reduced with help of adaptive and hybrid lifting scheme with PSO and artificial bee colony algorithm.

Keywords: Wavelet transform, lifting scheme, prediction function P, update function, Adaptive hybrid lifting scheme , PSO algorithm, ABC Algorithm, image compressionI .