手持GPU上图像处理算法的实现与优化

Nitin Singhal, I. Park, Sungdae Cho
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引用次数: 46

摘要

手持设备上带有可编程着色器的GPU的出现促使嵌入式应用程序开发人员利用GPU来卸载计算密集型任务,并减轻嵌入式CPU的负担。在这项工作中,我们提出了一个使用OpenGL ES 2.0 API的手持GPU可编程着色器的图像处理工具包。通过使用图像处理工具包,我们展示了一系列图像处理算法很容易映射到手持GPU。我们采用实时视频缩放,卡通风格的非真实感渲染和哈里斯角检测器作为我们的示例应用程序。此外,我们提出了通过优化着色器设计和在顶点和片段着色器之间有效共享GPU工作负载来提高性能的技术。性能是根据不同视频流分辨率下的每秒帧数来评估的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation and optimization of image processing algorithms on handheld GPU
The advent of GPUs with programmable shaders on handheld devices has motivated embedded application developers to utilize GPU to offload computationally intensive tasks and relieve the burden from embedded CPU. In this work, we propose an image processing toolkit on handheld GPU with programmable shaders using OpenGL ES 2.0 API. By using the image processing toolkit, we show that a range of image processing algorithms map readily to handheld GPU. We employ real-time video scaling, cartoon-style non-photorealistic rendering, and Harris corner detector as our example applications. In addition, we propose techniques to achieve increased performance with optimized shader design and efficient sharing of GPU workload between vertex and fragment shaders. Performance is evaluated in terms of frames per second at varying video stream resolution.
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