Image compression using clustering algorithms

There is a correlation between pixels in each image so that each pixel value of adjacent pixels can be guessed. By removing these dependencies can be compressed images. Our goal is to reduce the amount of compressed image data needed to display the digital images and therefore reduce the cost of transmission and storage. Compression has a key role in many important applications. These applications include image database, transmission of images, remote sensing, medical imaging, military and space equipment remote control and so on. In addition to the compression, image coding, there's talk. That after quantization matrix should be coded range of conversions. In reconstruction after decoding to achieve our desired image obtained with the difference that the picture is far less than the original image. What we've done in this thesis using a fractal method utilizes a Kohonen neural networks and clustering to increase the compression ratio and reduction coding and decoding the image. We have implemented three methods based on fractal coding. The first method is simple fractal coding. In the second method to create the codebook of multiple tree fractal coding is used. In the second method of vector quantization LBG algorithm for Kohonen neural network-based clustering algorithm and code book for coding image is used. Results in the second method show faster encoding. The method is simple fractal compression rate is higher than other methods.