Multispectral Image Compression Based on Fractal and K-Means Clustering

Image compression has been one of the main research topics in the field of image processing for a long time. Multispectral images are formed by a large number of component images of a single subject taken in different spectral windows. They are often represented by tens or even hundreds of Mbits of data and huge resources are required to transmit and store them, making some form of data compression necessary. To obtain high compression efficiency, exploiting both the spatial and the spectral dependency, we proposed a new combination of K-Means clustering, fractal image coding for multispectral image. The results obtained with our scheme are compared with schemes based on Wavelet-Fractal and are superior.