Object oriented framework for real-time image processing on GPU

In this paper, we present a framework for efficiently integrating programming resources of both GPU and CPU. We introduce an object oriented framework for GPGPU-based image processing. We illustrate a set of classes exploiting the design and programming advantages of an object oriented language, such as code reusability/extensibility, flexibility, information hiding, and complexity hiding. This class structure is supplemented with shader (GLSL) and kernel (CUDA) programming to facilitate full functionality. We demonstrate the potential of our approach with application scenarios and discuss the framework´s performance in terms of programming effort, execution overhead, and speedup factor achieved over CPU.