A binary partitioning approach to image compression using weighted finite automata for large images

Abstract

Fractal-based image compression techniques give efficient decoding time with primitive hardware requirements, and favor 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 bintree partitioning-is tested on the WFA encoding method. Experimental results show that binary partitioning consistently gives higher compression ratios than the conventional quadtree partitioning method for large images. 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 multilayered error protection over an often unreliable networking environment. Also, the proposed partitioning approach can be parallelized to reduce its high encoding complexity. © 2006 Elsevier Ltd. All rights reserved.