APPLYING BINARY PARTITIONING TO WEIGHTED FINITE AUTOMATA FOR IMAGE COMPRESSION (MP-P7)

Author(s) : Kai Yang (School of Computing, National University of Singapore, Singapore) Ghim Hwee Ong (School of Computing, National University of Singapore, Singapore)

Abstract : Fractal-based image compression techniques give efficient decoding time with primitive hardware requirements, which favors real-time communication purposes. One such technique, the Weighted Finite Automata (WFA) is studied on grayscale images. An improved image partitioning technique — the binary or bin-tree partitioning — is tested on the WFA encoding method. Experimental results show that binary partitioning consistently gives higher compression ratios than the conventional quad-tree partitioning method. Moreover, the ability to decode images progressively rendering finer and finer details can be used to display the image over a congested and loss-prone network such as the Image Transport Protocol (ITP) for the Internet, as well as to pave way for multi-layered error protection over an often unreliable networking environment such as the UDP.

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