Dictionary design for text image compression with JBIG2

The JBIG2 standard for lossy and lossless bilevel image coding is a very fl exible encoding strategy based on pattern matching techniques. This paper a ddresses the problem of compressing text images with JBIG2, For text image compression, JBIG2 allows two encoding strategies: SPM and PM&S, We compare in detail the lossless and lossy coding performance using the SPM-based an d PM&S-based JBIG2, including their coding efficiency reconstructed image q uality and system complexity. For the SPM-based JBIG2, we discuss the bit r ate tradeoff associated with symbol dictionary design. We propose two symbo l dictionary design techniques: the class-based and tree-based techniques. Experiments show that the SPM-based JBIG2 is a more efficient lossless syst em, leading to 8% higher compression ratios on average. It also provides be tter control over the reconstructed image quality in lossy compression. How ever, SPM's advantages come at the price of higher encoder complexity. The proposed class-based and tree-based symbol dictionary designs outperform si mpler dictionary formation techniques by 8% for lossless and 16-18% for los sy compression.