For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Implementation and Optimization of Image Processing Algorithms on Embedded GPU
Nitin SINGHAL Jin Woo YOO Ho Yeol CHOI In Kyu PARK
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2012/05/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing and Video Processing
embedded GPU, GPGPU, image processing, OpenGL ES 2.0, NPR, SURF, stereo matching,
Full Text: PDF(1.6MB)>>
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.