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Recent advances in computing such as the massively parallel GPUs (GraphicalProcessing Units),coupled with the need to store and deliver large quantitiesof digital data especially images, has brought a number of challenges forComputer Scientists, the research community and other stakeholders. Thesechallenges, such as prohibitively large costs to manipulate the digital dataamongst others, have been the focus of the research community in recent yearsand has led to the investigation of image compression techniques that canachieve excellent results. One such technique is the Discrete Cosine Transform,which helps separate an image into parts of differing frequencies and has theadvantage 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 Loeffleralgorithm for efficient image compression. The computational efficiency isanalyzed and evaluated under both the CPU and GPU. The PSNR (Peak Signal toNoise Ratio) is used to evaluate image reconstruction quality in this paper.The results are presented and discussed.

CUDA Based Performance Evaluation of the Computational Efficiency of the DCT Image Compression Technique on Both the CPU and GPU