Hybrid Implementation of Error Diffusion Dithering

A 8-bit RGB image at various levels of dithering per color channel. (From left - no dithering, 2 levels of dithering) More Images

People

Aditya Deshpande, Ishan Misra and P J Narayanan

Abstract

Many image filtering operations provide ample parallelism, but progressive non-linear processing of images is among the hardest to parallelize due to long, sequential, and non-linear data dependency. A typical example of such an operation is error diffusion dithering, exemplified by the Floyd-Steinberg algorithm. In this paper, we present its parallelization on multicore CPUs using a block-based approach and on the GPU using a pixel based approach. We also present a hybrid approach in which the CPU and the GPU operate in parallel during the computation. High Performance Computing has traditionally been associated with high end CPUs and GPUs. Our focus is on everyday computers such as laptops and desktops, where significant compute power is available on the GPU as on the CPU. Our implementation can dither an 8K X 8K image on an off-the-shelf laptop with an Nvidia 8600M GPU in about 400 milliseconds when the sequential implementation on its CPU took about 4 seconds.

Accepted in HiPC 2011

Aditya Deshpande, Ishan Misra, and P J Narayanan. Hybrid Implementation of Error Diffusion Dithering, Proceedings of International Conference on High Performance Computing (HiPC), 2011. [PDF] [BibTeX]

Copyright notice

The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder, except when identified by Creative Commons License 2.0, in which case the license applies to both the original and modified versions of the images.