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

TL;DR

This paper evaluates the performance of the DCT image compression technique implemented with CUDA on both CPU and GPU, analyzing efficiency and image quality using PSNR.

Contribution

It introduces a CUDA-based implementation of the Loeffler DCT algorithm and compares its computational efficiency on CPU and GPU.

Findings

GPU implementation shows higher efficiency than CPU.

PSNR results indicate comparable image quality between CPU and GPU implementations.

CUDA accelerates DCT-based image compression significantly.

Abstract

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.