Finite automata theory based methodics for the compression of two-dimensional gray-scale images is presented. Variable block-size segmentation procedure as well as techniques for the improvement of similarity of non-overlapping parts of the image are applied. Compression effect is attained by producing an image modeling finite automaton with minimal number of states.
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