【摘 要】
：
为了解决高清视频的畸变校正及显示的实时性问题,提出了一种CUDA架构下的并行加速方案。系统利用张正友标定方法获得摄像机的内部参数和畸变参数,并利用GPU的大规模并行计算能力加速校正过程。校正后,位于显存的图像数据直接利用OPENGL驱动进行显示。针对不同架构GPU片上资源限制不同,设计了一种并行划分参数自整定算法,保证了程序移植到不同GPU后能充分利用硬件资源,实现最佳性能。实验结果表明,本文设计
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为了解决高清视频的畸变校正及显示的实时性问题,提出了一种CUDA架构下的并行加速方案。系统利用张正友标定方法获得摄像机的内部参数和畸变参数,并利用GPU的大规模并行计算能力加速校正过程。校正后,位于显存的图像数据直接利用OPENGL驱动进行显示。针对不同架构GPU片上资源限制不同,设计了一种并行划分参数自整定算法,保证了程序移植到不同GPU后能充分利用硬件资源,实现最佳性能。实验结果表明,本文设计的系统对传统串行处理系统的综合加速比最高可达39倍以上,对2 596×1 920分辨率视频下的处理帧率可
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