CUDA Based Performance Evaluation of the Computational Efficiency of the DCT Image Compression Technique on Both the CPU and GPU
This work addresses performance challenges in image compression for researchers and practitioners, but it is incremental as it applies an existing method to GPU acceleration.
The paper evaluated the computational efficiency of implementing the DCT-based Cordic Loeffler algorithm for image compression using CUDA on both CPU and GPU, with results analyzed using PSNR for quality assessment.
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.