IMAGE-DATA COMPRESSION USING EDGE-OPTIMIZING ALGORITHM FOR WFA INFERENCE

Weighted finite automata (WFA) define real functions, in particular, g rayness functions of graytone images. Earlier, the authors gave an aut omatic encoding (inference) algorithm that converts an arbitrary funct ion (graytone image) into a WFA that can (approximately) regenerate it . The WFA obtained by this algorithm had (almost) minimal number of st ates, but a relatively large number of edges. Here we give an inferenc e algorithm that produces a WFA with not necessarily minimal number of states, but with a relatively small number of edges. Then we discuss image-data compression results based on the new inference algorithm al one and in combination with wavelets. It is a simpler and more efficie nt method than the other known fractal compression methods. It produce s better results than wavelets alone.