Efficient model based halftoning using direct binary search

Abstract

The direct binary search (DBS) algorithm is an iterative method which minimizes a metric of error between the grayscale original and halftone image. This is accomplished by adjusting an initial halftone until a local minimum of the metric is achieved at each pixel. The metric incorporates a model for the human visual system (HVS). In general, the DBS time complexity and halftone quality depend on three factors: the HVS model parameters, the choice of initial halftone, and the search strategy used to update the halftone. Despite the complexity of the DBS algorithm, it can be implemented with surprising efficiency. We demonstrate how the algorithm exploits the model for the HVS to efficiently yield very high quality halftones.