We propose and study in details a similarity enrichment scheme for the application to the image compression through the exten-sion of the weighted nite automata (WFA). We then develop a mech-anism with which rich families of legitimate similarity images can be systematically created so as to reduce the overall WFA size, leading to an eventual better WFA-based compression performance. A number of desirable properties, including WFA of minimum states, have been estab-lished for a class of packed WFA. Moreover, a codec based on a special extended WFA is implemented to exemplify explicitly the performance gain due to extended WFA under otherwise the same conditions.
CITATION STYLE
Jiang, Z., Litow, B., & de Vel, O. (2000). Similarity enrichment in image compression through weighted finite automata. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1858, pp. 447–456). Springer Verlag. https://doi.org/10.1007/3-540-44968-x_44
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