The JBIG2 binary image encoder dramatically improves compression ratios over previous encoders. The effectiveness of JBIG2 is largely due to its use of pattern matching techniques and symbol dictionaries for the representation of text. While dictionary design is critical to achieving high compression ratios, little research has been done in the optimization of dictionaries across stripes and pages.In this paper we propose a novel dynamic dictionary design that substantially improves JBIG2 compression ratios, particularly for multi-page documents. This dynamic dictionary updating scheme uses caching algorithms to more efficiently manage the symbol dictionary memory. Results show that the new dynamic symbol caching technique outperforms the best previous dictionary construction schemes by between 13% and 46% for lossy compression when encoding multi-page documents. In addition, we propose a fast and low-complexity pattern matching algorithm that is robust to substitution errors and achieves high compression ratios.
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