MC-JBIG2: An improved algorithm for Chinese textual image compression

Abstract

Standard JBIG2 algorithms for textual image compression focus on the features of alphabetic characters such as English, not considering the features of pictograph characters such as Chinese. In this work, an improved algorithm called MC-JBIG2 is developed, which aims at improving compression ratio for Chinese textual images. In the proposed method, first multiple features are extracted from the characters in the images. After that, a cascade of clusters is introduced to accomplish the pattern-matching task for the characters. Finally, to optimize the parameters used in the cascade of clusters, a Monte Carlo strategy is implemented to traverse the feasible space. Experimental results show MC-JBIG2 outperforms existing representative JBIG2 algorithms and systems on Chinese textual images. MC-JBIG2 can also improve compression ratio on Latin textual images, however, the improvement on Latin textual images is not as stable as the improvement on Chinese ones. © 2010 Springer-Verlag.