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Image compression techniques reduce the number of bits required to represent an image by taking advantage of these redundancies. An inverse process called decompression (decoding) is applied to the compressed data to get there constructed image. The objective of compression is to reduce the number of bits as much as possible, while keeping the resolution and the visual quality of the reconstructed image as close to the original image as possible. Image compression systems are composed of two distinct structural blocks: an encoder and a decoder. The existing method Huffman encoding technique is used for loss less compression. It has the drawbacks of Images like they are often used in prepress are better handled by other compression algorithms. It always produces rounding errors, because its code length is restricted to multiples of a bit. This deviation from the theoretical optimum is much higher. In this research propose a novel method for encoding namely Arithmetic Coding to provide lossless image compression. The proposed arithmetic coding provides the better Compression Ratio (CR) and lesser Bits Per Pixel (BPP). Though none of the technique can be considered as completely lossless but using these techniques the loss has been expected to be minimal. The proposed arithmetic coding is a entropy coder for lossless compression. It encodes the entire input data using the real interval. It is very efficient than the Huffman coding. In addition, proposed system uses the fractal compression is a lossless compression method for digital images, based on fractals. The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image. Enhanced Fractal algorithms convert these parts into mathematical data called "fractal codes" which are used to recreate the encoded image.