Block context modeling approach for binary image coding

Summary form only given. We address a new lossy coding method for binary images using high-order context model based on vector quantization (VQ). Binary images, such as engineering drawings and weather maps, usually contain structured patterns as well as large white space. In order to utilize structured information, the proposed approach employs the new block-based high-order context modeling. The advantage of having block context is a reduced complexity for the same amount of context coverage. In the proposed approach, the context is determined based on adjacent blocks. The number of block pattern is quite large even for a moderate block size. In practice, the probability distribution of all the block pattern is highly non-uniform. Therefore it is possible to reduce the number of contexts significantly without sacrificing compression performance. We use a node merging method reduce the number of block patterns for context nodes. The proposed compression algorithm utilizes two methods; a context model based binary VQ and JBIG algorithm. The compression results are good with the compression ratios ranging from 20 to 60. It was observed that the computer generated characters have better visual quality than hand written characters. The test images were engineering drawing maps for pipelines and CCITTT test images