International Journal of Computer Applications (0975 – 8887) Volume 34– No.2, November 2011 17 An Improved Algorithm of Fractal Image Compression ABSTRACT The need for compression is to minimize the storage space and reduction of transmission cost. When a digital image is transmitted through a communication channel, the cost of the transmission depends on the size of the data. The only way currently to improve on these resource requirements is to compress images such that they can be transmitted quicker and then decompressed by the receiver. There are many applications requiring image compression such as multimedia, internet, satellite imaging, remote sensing, preservation of art work, etc. Numerous methods for image compression have been presented in the literature survey but there is always a scope for improvement. In current work the fractal image compression has been employed as an efficient method in image compression. A novel compression encoding technique using hard threshold has been proposed based on fractal image compression and the results are compared with the other state of art image compression methods. The proposed method reduces the Encoding time significantly while some what compromising with the quality of the image. The initial experiments show that the proposed approach could achieve smaller encoding time and higher compression ratio on images. The proposed algorithm exhibits promising results from quantitatively and qualitatively points of view. Keywords Compression, Contractive transform, Fractal, Iterative Function System, Thresholding. 1. INTRODUCTION 1.1 Image Compression Compression is the process of reducing the size of a file or of a media such as high-tech graphical images etc, by encoding its data information more efficiently. By doing this, there is a reduction in the number of bits and bytes used to store the information. Therefore, a smaller file or image size is generated in order to achieve a faster transmission of electronic files or digital images and a smaller space required for downloading. Compression is done by using compression algorithms that rearrange and reorganize data information so that it can be stored economically. By encoding information, data can be stored using fewer bits. This is done by using a compression/decompression program that alters the structure of the data temporarily for transporting, reformatting, archiving, saving, etc. Compression reduces information by using different and more efficient ways of representing the information. Methods may include simply removing space characters, using a single character to identify a string of repeated characters or substituting smaller bit sequences for recurring characters. Some compression algorithms delete information altogether to achieve a smaller size. Depending on the algorithm used files can be greatly reduced from its original size. 1.2 Lossy vs Lossless Compression Depending on the detail present, compression can be categorized in two broad ways: Lossy Compression: Its aim is to obtain the best possible fidelity for a given bit rate or minimizing the bit rate to achieve a given fidelity measure. It reduces a file by permanently eliminating certain information especially redundant information. When the file is uncompressed, only a part of the original information is only present, although the user may not notice it. Used for images, video or sound where a certain amount of information loss will not be detected by most users and the loss of quality is affordable. Lossless Compression: In this data is compressed and can be reconstituted without loss of detail or information. This is referred to as bit- preserving or reversible compression systems. To achieve this, algorithms create reference points for things such as textual patterns, store them in a catalogue and send the along the smaller encoded file. When uncompressed, the file is regenerated by using those documented reference points to re- substitute the original information [2] [7]. It is a form of compression in which data files are split up into different chunks and reorganized to optimize them. This sort of compression very rarely saves much space, but it is ideal for transporting enormous files by breaking them into easier-to-handle pieces. Lossless compression is used when every bit of data is needed in the end product, often when transmitting a file to a designer. a lossless compression allows the designer to be sure that any data they may want to alter will be there, letting them create a final product before compressing the file further using a lossy compression. Lossless compression is ideal for documents containing text and numerical data where any loss of textual information can not be tolerated. The advantage of lossy methods over lossless methods is that in some cases a lossy method can produce a much smaller compressed file than any lossless method, while still meeting the requirements of the application. If an image is compressed, it needs to be uncompressed before it can be viewed. Some processing of data may be possible in encoded form. Lossless compression involves some form of entropy encoding and is based in information theoretic techniques whereas lossy compression use source encoding techniques that may involve transform encoding, differential encoding or vector quantization. Anupam Garg Bhai Gurdas Institute of Engineering & Technology, Sangrur