An inference implementation based on extended weighted finite automata
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Similarity Enrichment in Image Compression through Weighted Finite Automata
COCOON '00 Proceedings of the 6th Annual International Conference on Computing and Combinatorics
Unification and extension of weighted finite automata applicable to image compression
Theoretical Computer Science
Weighted Finite Automata encoding over Thai language
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
On generalizations of weighted finite automata and graphics applications
CAI'07 Proceedings of the 2nd international conference on Algebraic informatics
| Hi-index | 0.00 |
Weighted finite automata (WFA) generalize finite automata by attaching real numbers as weights to states and transitions. As shown by Culik and Kari (1994, 1995) WFA provide a powerful tool for image generation and compression. The inference algorithm for WFA subdivides an image into a set of nonoverlapping range images and then separately approximates each one with a linear combination of the domain images. In the current paper we introduce an improved definition for WFA that increases the approximation quality significantly, clearly outperforming the JPEG image compression standard. This is achieved by the bintree partitioning of the image and by appending not only two adjacent range images but also every single range image to the pool of domain images. Moreover, we present a new lossless entropy coding module that achieves efficient and fast storing and retrieving of the WFA coefficients.