The partitioned iterated function systems (PIFS) fractal image compression technique provides very competitive rate-distortion curves and fast decoding. However, it suffers from complicated encoding computation. Three novel neural network techniques, mixture of nonlinear principal components (MNLPC), mixture of independent components (MIC) and high-dimensional mixture of principal components (H-MPC) are developed to reduce the encoding complexity of the PIFS fractal coding. Applying these new techniques, the potential best range-domain matching search is confined to a relatively small size domain block pool. Using the new techniques, the encoding time is shortened dramatically, and the compression performance is improved as well.
['Baoguo Xie', 'Robert D. Dony']
Fractal image compression using MNLPC, MIC and H-MPC network library