D.J. Ashpin Pabi.et.al. Int. Journal of Engineering Research and Application www.ijera.com ISSN : 2248-9622, Vol. 6, Issue 10, ( Part -5) October 2016, pp.24-31 www.ijera.com 24 | Page Image Compression based on DCT and BPSO for MRI and Standard Images D.J. Ashpin Pabi 1 , M. Mahasree 2 , P. Aruna 3 , N.Puviarasan 4 1 (Research Scholar, Dept. of CSE, Annamalai University, Chidambaram, Tamil Nadu, India 2 (PG Scholar, Dept. of CSE, Annamalai University, Chidambaram, Tamil Nadu, India 3 (Professor, Dept. of CSE, Annamalai University, Chidambaram, Tamil Nadu, India 4 (Associate Professor, Dept. of CSE, Annamalai University, Chidambaram, Tamil Nadu, India ABSTRACT Nowadays, digital image compression has become a crucial factor of modern telecommunication systems. Image compression is the process of reducing total bits required to represent an image by reducing redundancies while preserving the image quality as much as possible. Various applications including internet, multimedia, satellite imaging, medical imaging uses image compression in order to store and transmit images in an efficient manner. Selection of compression technique is an application-specific process. In this paper, an improved compression technique based on Butterfly-Particle Swarm Optimization (BPSO) is proposed. BPSO is an intelligence-based iterative algorithm utilized for finding optimal solution from a set of possible values. The dominant factors of BPSO over other optimization techniques are higher convergence rate, searching ability and overall performance. The proposed technique divides the input image into 8 8  blocks. Discrete Cosine Transform (DCT) is applied to each block to obtain the coefficients. Then, the threshold values are obtained from BPSO. Based on this threshold, values of the coefficients are modified. Finally, quantization followed by the Huffman encoding is used to encode the image. Experimental results show the effectiveness of the proposed method over the existing method. Keywords: BPSO, DCT, Huffman encoding, quantization, threshold I. INTRODUCTION The main aim of image compression systems is to reduce the memory required to store the images; and transfer the images over longer distances with reduced cost and time. In some of the applications of security, reducing the transfer time may also decrease the chances of security attacks. The compression can be achieved when there are redundancies in the image. Redundancy is the term which denotes the presence of some irrelevant or repeated data in an image. Generally, there are three types of redundancy occurs in images [1]. They are 1) Coding redundancy: It emphasizes the process of bit allocation. When the codes (pixel values) are allotted with more bits than actually required, coding redundancy occurs. 2) Inter-pixel redundancy: Most of the images have neighboring pixels with highly correlated values, in other words, there are some larger regions in an image where the pixel values are almost the same. This is known as inter-pixel redundancy. 3) Psycho-visual redundancy: There are some slight intensity variations in images that cannot be differentiated by human eye. These visual variations which are less relevant to observer are called psycho-visual redundancy. To achieve compression these redundancies has to be removed. Fig.1 shows the standard image compression system [2]. It is composed of two functional components: an encoder and a decoder. The original image as ) , ( j i f is fed into the encoder of the compression system. The encoder removes the redundancies through a series of three independent operations 1) Mapper: It transforms the input image into a non-visual format designed to reduce inter- pixel redundancy 2) Quantizer: It quantizes the pixel values by eliminating less relevant information while preserving highly sensitive information 3) Symbol coder: It generates a fixed or variable length code to represent the quantized output and maps the output in accordance with the code. By doing so, it reduces coding redundancy. The decoder contains three components: symbol decoder, dequantizer and inverse mapper. They perform the reverse operations of encoder and reconstructed image ) , ( ' j i f is obtained as the output. Fig.1. Block diagram of an image compression system: a) Encoder b) Decoder RESEARCH ARTICLE OPEN ACCESS