Look up table (LUT) method for image halftoning

We have previously applied the look up table (LUT) method for inverse halftoning. We also proposed a tree-structure LUT inverse halftoning in order to reduce the memory requirements of the LUT method. We introduce the LUT based halftoning method. Pixels from a causal neighborhood and the contone value of the current pixel are included in the LUT. The LUT halftoning requires no arithmetic operations other than memory access. For any halftoning method, a sample set of images and halftones of these images is used. We introduce the tree-structure LUT (TLUT) halftoning. Even though this method is more complex than LUT halftoning it produces better halftones and it requires much less storage than LUT halftoning. We demonstrate how the error diffusion characteristics can be achieved with this method. The algorithm is trained on halftones obtained by direct binary search (DBS) images. The complexity of the tree-structure LUT halftoning is higher than the error diffusion algorithm but much lower than the DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of TLUT gets larger. Thus, the halftone image quality between the error diffusion and DBS is achieved depending on the size of the tree-structure in the TLUT algorithm