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   Vol.E95-D   No.5   pp.1475-1484
Publication Date: 2012/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1475
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,  

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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.