Color Image Compression with Modified Fractal Coding on Spiral Architecture Nileshsingh V. Thakur, Department of Computer Science, G.H. Raisoni College of Engineering, Nagpur, India thakurnisvis@rediffmail.com Dr. O. G. Kakde Department of Computer Science, Visvesvaraya National Institute of Technology, Nagpur, India ogkakde@vnitnagpur.ac.in Abstract—The proposed approach (CICMFCSA), firstly, compose the one-plane image using the pixel’s trichromatic coefficients. One-plane image in traditional square structure is represented in Spiral Architecture for compression. On this Spiral Architecture image, proposed modified Fractal grey level image coding algorithm (MFCSA) is applied to get encoded image. In this modified Fractal coding, the numbers of domain blocks are optimized from 343 to 10 using local search. Extensive experiments are carried out on UCID - An Uncompressed Color Image Database. The proposed approach minimizes the encoding process time because of optimized domain blocks and one dimensional structure of Spiral Architecture and falls in the lossy compression category. The results of SFC and our approach are compared with respect to the time. Index Terms—Image Compression, Spiral Architecture, Fractal Coding I. INTRODUCTION Information transmission is the key means to acquire and give the knowledge or data related to particular event. For example: video conferences, medical data transfer, business data transfer, etc. require much more image data to be transmitted and stored on-line. Due to the Internet, the huge information transmissions take place. The processed data required much more storage, computer processor speed and much more bandwidth for transmission. To overcome these problems, image compression is necessary. The whole process of image compression minds the fact that images are nature- generated and that the human eye may not perceive the details possibly lost during this type of image codification process. Compressing an image is significantly different than compressing raw binary data. Of course, general purpose compression programs can be used to compress images, but the result is less than optimal. This is because images have certain statistical properties which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image can be sacrificed for the sake of saving a little more bandwidth or storage space. Image data redundancy is a key for image compression. Most images contain some amount of redundancy that can sometimes be removed when the image is stored and replaced when it is reconstructed, but eliminating this redundancy does not lead to high compression. Fortunately the eye is insensitive to a wide variety of information loss. Fractal coding method exploits similarities in different parts of the image. Fractal objects like Sierpinski triangle and Fern {[1], [2], [3]} have very high visual complexity and low storage information content. For generating computer graphic images and compression of such objects, Iterated Function System (IFS) {[2], [4], [5]} are recently being used. The basic idea is to represent an image as the fixed points of IFSs. An appropriately chosen IFS consists of a group of affine transformations [3]. Therefore, an input image can virtually be represented by a series of IFS codes. In short, for fractal coding an image is represented by fractals rather than pixels. Each fractal is defined by a unique IFS consists of a group of affine transformations. Therefore the key point for fractal coding is to find fractals which can best approximate the original image and then to represent them as a set of affine transformations. In all, the fractal coding is always applied to grey level images. The most straight forward method to encode a color image by gray-level fractal image coding algorithm is to split the RGB color image into three Channels, red, green and blue, and compress them separately by treating each color component as a single gray-scale image, the so called three-component Seperated Fractal Coding (SFC). In place of going for three independent planes, in this paper, we have composed a one plane image from the three planes of RGB color image using trichromatic coefficients. This one plane image is then compressed by proposed modified Fractal coding on Spiral Architecture, which minimizes the the number of domain blocks from Based on “Color Image Compression on Spiral Architecture using Optimized Domain Blocks in Fractal Coding”, by Nileshsingh V. Thakur and Dr. O. G. Kakde, which appeared in the Proceedings of the IEEE International Conference on Information Technology: New Generations ITNG 2007, Las Vegas, USA, April 2007. © 2007 IEEE. JOURNAL OF MULTIMEDIA, VOL. 2, NO. 4, AUGUST 2007 55 © 2007 ACADEMY PUBLISHER