IFS coding using an MPC network library

This paper examines the relationship between iterated function systems (IFS), a fractal approach to image compression, and the mixture of principal components (MPC), a neural network approach to image compression. Both can be fundamentally expressed as a local linear transformation. In IFS, the basis vector comes from the image itself and evolves during the iterations while in MPC, the trained network contains the basis vectors. A new method of image compression is presented which uses an MPC network as a library for reducing the search for the large domain blocks in IFS. The resulting hybrid approach has better rate-distortion characteristics relative to standard IFS when tested on a standard image