基于OpenCL的JPEG压缩算法并行化设计与实现

Parallel design and implementation of JPEG compression algorithm based on OpenCL

随着图像数据的大量增加,传统单处理器或多处理器结构的计算设备已无法满足实时性数据处理要求。异构并行计算技术因其高效的计算效率和并行的实时性数据处理能力,正得到广泛关注和应用。利用GPU在图形图像处理方面并行性的优势,提出了基于OpenCL的JPEG压缩算法并行化设计方法。将JPEG算法功能分解为多个内核程序,内核之间通过事件信息传递进行顺序控制,并在GPU+CPU的异构平台上完成了并行算法的仿真验证。实验结果表明,与CPU串行处理方式相比,本文提出的并行化算法在保持相同图像质量情况下有效提高了算法的执行效率,大幅降低了算法的执行时间,并且随着图形尺寸的增加,算法效率获得明显的提升。

As the scale of information data increases enormously, traditional single-processor or mul- tiprocessor structure based computing devices are unable to meet the requirements of real-time data pro- cessing. The heterogeneous parallel computing technology attracts much attention and is widely applied for its effective computation efficiency and parallel real-time processing capability. We propose a parallel design of the JPEG compression algorithm based on OpenCL by using the advantages of the GPU in im- age processing. The JPEG algorithm is divided into multiple kernel programs, and the kernels are se- quentially controlled by the event information transfer. The parallel algorithm is simulated and verified on the GPU＋CPU platform. Experimental results show that under the same image quality condition, the parallel algorithm can improve algorithm implementation efficiency and reduce time substantially. And as the graph size increases, the efficiency of the algorithm obtains obvious improvement.