Quaternion moments are widely used in several applications, such as image classification, object recognition, and multimedia security. The computation of these moments requires a vast computational time, especially for big-size images. Several attempts to accelerate quaternion moments are not enough to process big-size color images with the desired speedup. In this work, we proposed a new parallel framework for fast computation of quaternion moments in Cartesian coordinates using multi-core CPUs and many-core graphics processing units (GPUs) with the Compute Unified Device Architecture (CUDA). We called the proposed unified computational framework “CUDAQuat.” This framework was tested by eleven sets of quaternion moments. Several applications executed using the proposed parallel framework where the CPU times, execution-time-improvement ratio, and speedup were reported. The evaluation outlined significant speedup over the single-core CPU implementation, where the proposed framework accelerated several sets of quaternion moments with speedup 600x.

四元数矩已广泛用于多种应用中，例如图像分类，对象识别和多媒体安全性。这些力矩的计算需要大量的计算时间，尤其是对于大尺寸图像。加快四元数矩的几种尝试不足以以所需的速度处理大尺寸的彩色图像。在这项工作中，我们提出了一个新的并行框架，该框架可使用具有计算统一设备体系结构（CUDA）的多核CPU和多核图形处理单元（GPU）快速计算笛卡尔坐标系中的四元数矩。我们将提议的统一计算框架称为“ CUDAQuat”。该框架已通过11组四元数矩进行了测试。使用建议的并行框架执行了多个应用程序，其中CPU时间，执行时间改进率，和加速的报道。评估概述​​了单核CPU实施的显着提速，其中建议的框架以600倍的提速速度加速了几组四元数矩。