Ifs Coding Using an Mpc Network Library

Robert D. Dony1 Edward R. Vrscay2 1School of Engineering University of Guelph Guelph, ON N1G 2W1 email: [email protected] 2Deptartment of Applied Mathematics University of Waterloo Waterloo, ON N2L 2G1 email: [email protected] ABSTRACT This paper examines the relationship between iterated function systems (IFS), a fractal approach to image compression, and mixutre 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 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.