Speeding up fractal image coding by combined DCT and Kohonen neural net method

摘要

Iterated transformation theory (ITT) coding, also known as fractal coding, in its original form, allows fast decoding but suffers from long encoding times. During the encoding step, a large number of block best-matching searches have to be performed which leads to a computationally expensive process. We present a new method that significantly reduces the computational load of ITT based image coding. Both domain and range blocks of the image are transformed into the frequency space. Domain blocks are then used to train a two dimensional Kohonen (1982, 1989, 1990) neural network (KNN) forming a code book similar to vector quantization coding. The property of KNN (and self-organizing feature maps in general) which maintains the topology of the input space allows to perform a neighboring search so as to find the piecewise transformation between domain and range blocks.