基于ARM的图像几何变换算法库实现和优化技术研究

Study on Implementation and Optimization of ARM-based Image Geometric Transformation Library

高性能原语基础算法库(Intel■Integrated Performance Primitives, Intel IPP)是面向信号、图像处理领域的高性能多媒体加速库。然而,截至目前,暂时没有基于ARM架构的高性能IPP库。文中针对镜像变换、重映射、仿射、透视变换等基础图像几何变换算法,实现了一个基于ARM计算平台的高性能算法库PerfIPP,并通过SIMD汇编优化、内存对齐、数据预计算、高性能矩阵转置等优化技术,显著提升了上述算法的性能。同时,通过对比不同指令组合、不同指令排列、不同取数存储方式等所带来的性能差异,总结图像几何变换算法在ARM计算平台上实现与优化的关键技术。实验结果表明,在华为鲲鹏920平台上,相比开源计算机视觉库OpenCV,PerfIPP在满足精度要求的同时,在上述基础图像几何变换上获得了108.08%~435.5%的性能提升,并达到了在英特尔至强E5-2640处理器上Intel IPP库平均性能的83.79%。

Intel■ integrated performance primitives is a high-performance multimedia acceleration library for signal and image processing.However, as of now, there is no high-performance IPP library based on the ARM architecture.This paper implements a high-performance algorithm library PerfIPP based on the ARM computing platform for basic image geometric transformation algorithms such as mirror, remap, and affine/perspective transformation.The PerfIPP,optimized through SIMD assembly, memory alignment, data pre-calculation, high-performance matrix optimization techniques, has significantly improved the performance of the above algorithms.At the same time, This paper summarizes the key technologies for the realization and optimization of image geometric transformation algorithms on the ARM computing platform by comparing the performance differences brought about by different instruction combinations, different instruction arrangements, and different access and storage methods.Experimental results show that, on the Huawei Kunpeng 920 platform, the PerfIPP proposed in this paper can achieve 108.08%~435.5% performance improvement in image transformation compared with the open source computer vision library while meeting accuracy.It also achieves 83.79% of the average performance of Intel IPP library on Intel Xeon E5-2640 processor.