A New Approach to Fractal Image Compression Using DBSCAN Jaseela C C and Ajay James Dept. of Computer Science & Engineering, Govt. Engineering College, Trichur, Thrissur, India Email: cp.jesi@gmail.com, ajayjames80@gmail.com Abstract—Fractal image compression is a popular lossy image compression technique. Fractal encoding, a mathematical process to encode image as a set of mathematical data that describing fractal properties of the image. Fractal image compression works based on the fact that all natural, and most artificial, objects contain similar, repeating patterns called fractals. But problem is the encoding of fractal image compression takes lot of time and it is computationally expensive. A large number of sequential searches are required to find matching of portions of the image. In this paper we introduce a new algorithm to fractal image compression by using density based spatial clustering of applications with noise (DBSCAN). The modification is applied to decrease encoding time by reducing the sequential searches through the whole image to its neighbors. This method compress and decompress the color images quickly. Index Terms—fractal, DBSCAN, fractal image compression, RGB, clustering, algorithm I. INTRODUCTION The need for mass information storage and fast communication links grows day by day. Storing images in less memory leads to a direct reduction in storage cost and faster data transmissions. Fractal compression is a popular lossy image compression technique using fractals [1]. There are two sorts of image compression [2], lossy image compression and lossless image compression. In lossless image compression, it is possible to go back to the original data from the compressed data and there is no loss of information. In most cases it doesn't matter if the image is changed a little without causing any noticeable difference [3]. Lossy image compression works by removing redundancy and by creating an approximation to the original with high compression ratio. Fractal image compression works based on the fact that all natural, and most artificial, objects contain similar, repeating patterns called fractals. Fractal encoding, a mathematical process to encode image as a set of mathematical data that describing fractal properties of the image [4]. A fractal is a rough geometric shape that is made up of similar forms and patterns [5]. Fractal encoding is highly used to convert bitmap images to fractal codes. Fractal decoding is just the reverse, in which a set of fractal codes are converted to a Manuscript received July 15, 2013; revised September 5, 2013. bitmap [6]. The encoding process is extremely computationally intensive. Millions or billions of iterations are required to find the fractal patterns in an image. Depending upon the resolution and contents of the input bitmap data, and output quality, compression time, and file size parameters selected, compressing a single image could take anywhere from a few seconds to a few hours (or more) on even a very fast computer [7]. Decoding a fractal image is a much simpler process. The hard work was performed finding all the fractals during the encoding process. All the decoding process needs to do is to interpret the fractal codes and translate them into a bitmap image. The remainder of the paper is organized as follows. Section II introduces fractal image compression. Section III describes our proposed method in detail. Section IV presents the experimental results. Conclusion is given in Section V. II. FRACTAL IMAGE COMPRESSION As a product of the study of iterated function systems (IFS), Barnsley and Jacquin introduced Fractal image compression techniques. They store images as quantized transform coefficients. Fractal block coders, as described by Jacquin, assume that “image redundancy can be efficiently exploited through self-transformability on a block wise basis” [8]. They store images as contraction maps of which the images are approximate fixed points. Images are decoded by iterating these maps to their fixed points. Fractal encoding works based on the fact that all objects have information in the form of similar, repeating patterns called an attractor [9]. The encoding process consists of finding the larger blocks, called domain blocks, of the image corresponding to the small image blocks, called range blocks through some operations. Fractal compression is highly suited for use in image databases and CD-ROM applications. In fractal image compression, dividing the image into non overlapping BXB blocks called range and divide image into overlapping 2B x 2B blocks called domain [10]. Then for each range block, search for the domain block that most closely resembles the range block. Transformations such as scaling, translation, rotating, sharing, scaling etc and adjustment of brightness/contrast are used on the domain block in order to get the best match. The color, brightness, contrast adjustments and the translation done on the domain blocks to match the position of their associated International Journal of Electrical Energy, Vol. 2, No. 1, March 2014 ©2014 Engineering and Technology Publishing 18 doi: 10.12720/ijoee.2.1.18-22