Image-data compression using edge-optimizing algorithm for WFA inference

Weighted finite automata (WFA) define real functions, in particular, grayness functions of graytone images. Earlier, the authors gave an automatic encoding (inference) algorithm that converts an arbitrary function (graytone image) into a WFA that can (approximately) regenerate it. The WFA obtained by this algorithm had (almost) minimal number of states, but a relatively large number of edges. Here we give an inference 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 alone and in combination with wavelets. It is a simpler and more efficient method than the other known fractal compression methods. It produces better results than wavelets alone.