GPU-enabled parallel processing for image halftoning applications

Programmable Graphics Processing Unit (GPU) has emerged as a powerful parallel processing architecture for various applications requiring a large amount of CPU cycles. In this paper, we study the feasibility for using this architecture for image halftoning, in particular implementing computationally intensive neighborhood halftoning algorithms such as error diffusion and Direct Binary Search (DBS). We show that it is possible to deliver very high performance even for high speed printers.