Fast and memory efficient text image compression with JBIG2

We investigate ways to reduce encoding time, memory consumption and substitution errors for text image compression with JBIG2. We first look at page striping where the encoder splits the input image into horizontal stripes and processes one stripe at a time. We propose dynamic dictionary updating procedures for page striping to reduce the bit rate penalty it incurs. Experiments show that splitting the image into two stripes can save 30% of encoding time and 40% of physical memory with a small coding loss of about 1.5%. Using more stripes brings further savings in time and memory but the return diminishes. We also propose an adaptive way to update the dictionary only when it has become out-of-date. The adaptive updating scheme can resolve the time versus bit rate tradeoff and the memory versus bit rate tradeoff well simultaneously. We then propose three speedup techniques for pattern matching, the most time-consuming encoding activity in JBIG2. When combined together, these speedup techniques can save up to 75% of the total encoding time with at most 1.7% of bit rate penalty. Finally, we look at improving reconstructed image quality for lossy compression. We propose enhanced prescreening and feature monitored shape unifying to significantly reduce substitution errors in the reconstructed images.