Chapter 34 - Experiences on Image and Video Processing with CUDA and OpenCL

Abstract

This chapter addresses the technical challenges and experiences associated with the domain-related algorithms implemented on GPU architectures specifically by using CUDA and OpenCL with an emphasis on real-time issues and optimization. The importance of GPUs has recently been recognized for general-purpose applications such as video and image processing algorithms. An increasing number of studies show substantial performance gains with their GPU-adapted implementations. This chapter implements two image and video processing applications on the CPU and the GPU to compare their effectiveness. As a video processing application, adaptive background subtraction, and as an image processing application, Pearson's correlation algorithms have been implemented. A series of implementations on background subtraction and correlation algorithms are presented, each of which addresses different aspects of GPU programming issues, including I/O operations, coalesced memory use, and kernel granularity. The experiments show that effective consideration of such design issues improves the performance of the algorithms significantly. This study aims to guide users in implementing GPU-based algorithms using CUDA and OpenCL architectures by providing practical suggestions. In addition, these two architectures are compared to demonstrate their advantages over each other, in particular, for video and image processing applications.