CUDA Based Performance Evaluation of the Computational Efficiency of the DCT Image Compression Technique on Both the CPU and GPU
School of Mathematics and Physical Sciences, North West University, Mafikeng Campus, Private Bag X2046, Mmabatho, 2735, South Africa
arXiv:1306.1373 [cs.DC], (6 Jun 2013)
@article{2013arXiv1306.1373M,
author={Mathews Modieginyane}, K. and {Polite Ncube}, Z. and {Gasela}, N.},
title={"{CUDA Based Performance Evaluation of the Computational Efficiency of the DCT Image Compression Technique on Both the CPU and GPU}"},
journal={ArXiv e-prints},
archivePrefix={"arXiv"},
eprint={1306.1373},
primaryClass={"cs.DC"},
keywords={Computer Science – Distributed, Parallel, and Cluster Computing},
year={2013},
month={jun},
adsurl={http://adsabs.harvard.edu/abs/2013arXiv1306.1373M},
adsnote={Provided by the SAO/NASA Astrophysics Data System}
}
Recent advances in computing such as the massively parallel GPUs (Graphical Processing Units),coupled with the need to store and deliver large quantities of digital data especially images, has brought a number of challenges for Computer Scientists, the research community and other stakeholders. These challenges, such as prohibitively large costs to manipulate the digital data amongst others, have been the focus of the research community in recent years and has led to the investigation of image compression techniques that can achieve excellent results. One such technique is the Discrete Cosine Transform, which helps separate an image into parts of differing frequencies and has the advantage of excellent energy-compaction. This paper investigates the use of the Compute Unified Device Architecture (CUDA) programming model to implement the DCT based Cordic based Loeffler algorithm for efficient image compression. The computational efficiency is analyzed and evaluated under both the CPU and GPU. The PSNR (Peak Signal to Noise Ratio) is used to evaluate image reconstruction quality in this paper. The results are presented and discussed.
June 7, 2013 by hgpu
