Performance Comparison Between OpenCV Built in CPU and GPU Functions on Image Processing Operations

Year 2017, Volume: 1 Issue: 2, 34 - 41, 30.06.2017

Batuhan Hangün , Önder Eyecioğlu

Abstract

 Image Processing is a specialized area of Digital
Signal Processing which contains various mathematical and algebraic operations
such as matrix inversion, transpose of matrix, derivative, convolution, Fourier
Transform etc. Operations like those require higher computational capabilities
than daily usage purposes of computers. At that point, with increased image
sizes and more complex operations, CPUs may be unsatisfactory since they use
Serial Processing by default. GPUs are the solution that come up with greater
speed compared to CPUs because of their Parallel Processing/Computation nature.
A parallel computing platform and programming model named CUDA was created by
NVIDIA and implemented by the graphics processing units (GPUs) which were
produced by them. In this paper, computing performance of some commonly used
Image Processing operations will be compared on OpenCV's built in CPU and GPU
functions that use CUDA.

Keywords

Image Processing, CUDA, Parallel Processing, OpenCV, GPU

References

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• [2] Alan V. Oppenheim, Alan S. Willsky, S. Hamid, Signals and Systems (2nd Edition), Pearson 1996
• [3] SMITH, W, STEVEN, A Scientist’s and Engineers’s Guide to DSP. 1997