Binary tree context modeling of halftone images using a fast adaptive template selection scheme

Recently, new applications such as printing on demand and personalized printing have arisen where lossless halftone image compression can be useful for increasing transmission speed and lowering storage costs. State-of-the-art lossless bilevel image compression schemes like JBIG achieve only moderate compression ratios because they do not fully take into account the special image characteristics. In this paper, we present an improvement on the context modeling scheme by adapting the context template to the periodic structure of the classical halftone image. This is a non-trivial problem for which we propose a fast close-to-optimal context template selection scheme based on the sorted autocorrelation function of a part of the image. We have experimented with classical halftones of different resolutions and sizes and screened under different angles as well as with stochastic halftones. For classical halftones, the global improvement with respect to JBIG in its best mode is about 30% to 50%; binary tree modeling increases this by another 5% to 10%. For stochastic halftones, the autocorrelation-based template gives no improvement, though an exhaustive search technique shows that even bigger improvements are feasible using the context modeling technique; introducing binary tree modeling increases the compression ratio with about 10%.