Implementation and Optimization of Image Processing Algorithms on Embedded GPU
Digital Media & Communication R&D Center, Samsung Electronics Co. Ltd., Suwon 443-742, Korea
IEICE TRANSACTIONS on Information and Systems, Vol.E95-D, No.5, pp.1475-1484, 2012
@article{singhal2012implementation,
title={Implementation and Optimization of Image Processing Algorithms on Embedded GPU},
author={SINGHAL, N. and YOO, J.W. and CHOI, H.Y. and PARK, I.K.},
journal={IEICE TRANSACTIONS on Information and Systems},
volume={95},
number={5},
pages={1475–1484},
year={2012},
publisher={The Institute of Electronics, Information and Communication Engineers}
}
In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU.
May 7, 2012 by hgpu
