“Design and optimization of image processing algorithms on mobile GPU” by Singhal, Yoo, Choi and Park

The advent of GPUs with programmable shaders on mobile phones has motivated developers to utilize GPU to offload computationally intensive tasks and relive the burden of embedded CPU. In this paper, we present a set of metrics to measure characteristics of a mobile phone GPU with the focus on image processing algorithms. These measures assist users in design and implementation stage and in classifying bottlenecks. 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), belief propagation (BP) stereo matching [Yang et al. 2006], and speeded up robust features (SURF) detection [Bay et al. 2008] as our example algorithms.

1. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. 2008. SURF: Speeded up robust features. Computer Vision and Image Understanding 110, 3, 346–359.
2. Yang, Q., Wang, L., Yang, R., Wang, S., Liao, M., and Nistér, D. 2006. Real-time global stereo matching using hierarchical belief propagation. In Proc. British Machine Vision Conference, 989–998.