【作者】 拱慧璇；

【导师】 赵彬；

【作者基本信息】 哈尔滨工业大学 ， 信息与通信工程， 2011， 硕士

【摘要】 随着数字化技术的快速发展,从静态图像到动态视频图像的数据量都大幅度增加,因此,目前技术领域中关注的热点是,在保证质量的情况下,如何减少图像数据的冗余信息,能够更加有效的存储或实时传输数据信息。现在CPU上运行的多数的压缩算法由于数据量的增加以及计算复杂度的提高,而不能满足实时需求。到目前为止,NIVIDIA的GPU已经发展过了八代,GPU在高性能通用计算领域发展中逐渐占据了主流地位,并且该技术的应用和发展一直呈现稳定且强劲的增长趋势。GPU的特点是对大量密集型数据并行处理运算。因此,本文主要研究的内容就是利用GPU实现静态图像JPEG压缩编码和基于MPEG-2的视频图像压缩编码。本文首先阐述了CUDA的编程模型,从主机与设备的关系,内核函数的使用方法到CUDA的线程层次;分析了CUDA的存储模型。并以GeForce GT240为例,分析了GPU的硬件架构和硬件映射,以及warp的发射和执行。以此为基础,展开GPU在图像编码方面的研究和实现。本文采用的是CPU+GPU的架构模型,CPU负责处理逻辑性较强的串行工作,而GPU则负责计算工作量较大的并行处理工作。这两者各司其职,合力完成图像压缩的任务。本文主要研究了基于GPU的JPEG静态图像的压缩编码和基于GPU的MPEG-2视频图像压缩编码。本文实现了在GPU上进行并行JPEG图像压缩编码。在对原JPEG编码算法的研究分析基础上,提出了适合在CUDA平台上进行并行运算的JPEG编码算法,并给出了在GPU上的优化。其中,最为重要的是研究了适合在GPU上实现的可并行DCT变换方法,并且对于熵编码,也分析了Huffman编码方法。本文在实现了基于GPU的JPEG图像压缩编码,并从几个方面对该算法进行了分析,说明了基于GPU的并行压缩的可行性。本文还实现了基于GPU的MPEG-2视频图像压缩编码。分析了MPEG-2视频压缩编码的基本原理。并进一步分析了MPEG-2视频压缩编码在GPU上并行运算的可行性,并提出了CPU+GPU的并行运算和CUDA中的两级并行运算。接着详细的研究了MPEG-2关键模块,包括运动估计、运动补偿、比较计算、变换与反变换、量化与反量化、熵编码,按照原算法的特性以及在基于GPU的CUDA编程模型的特点,提出了适合在GPU上的运算并行方法,分析各个模块的并行算法流程和CUDA编程模型的并行资源分布与图像处理单元的对应情况。给出了实现GPU上的MPEG-2视频图像压缩的实验环境,并从压缩率、峰值信噪比、编码效率几方面对整体并行压缩算法性能进行了分析,得出了本文的方法具有相对较好的结果。并且还对几个模块的编码速度进行了详细的分析。更多还原

【Abstract】 With the rapid development of digital technology, the data increased greatly in both static image and dynamic video image. It is noticeable how to decrease the redundant data in order to save or transmit information more efficiently. So far the GPU of NIVIDIA has evolved into the eighth generation, which increasingly dominates the high-powered general purpose computer field. This essay is written to show the way GPU encodes the image.At the beginning, I describe the model of CUDA, which is mainly made up of CPU+GPU. CPU is in charge of the serial work which is highly logical, and as for GPU, the parallel processing which costs more workload in computing.In this paper, a JPEG image compression coding system based on GPU is realized. Based on the analysis of the original JPEG coding algorithm, a concurrent JPEG coding algorithm which is applicable to CUDA platform is proposed. Most important of all, the paper studies the parallel DCT transformation which can be applied based on GPU and analyzes the parallel Huffman coding method. The paper realizes JPEG image compression coding method based on GPU and analyzes the algorithms through several aspects, and indicates the feasibility of GPU concurrent compression.In this paper, the MPEG-2 compressing and coding of video image based on GPU is also realized, and the basic principle of MPEG-2 is analyzed. The paper further researches on the feasibility of the concurrent algorithm and proposes the concurrent algorithm of CPU+GPU and 2-level concurrent algorithm of CUDA. In addition, the key module of MPEG-2 is studied in detail. The paper proposes the concurrent algorithm based on GPU and analyzes the flow of each module, the data distribution of CUDA programming model and the corresponding image processing unit. The paper analyzes the integral performance of concurrent compressing algorithm through compressing ratio, PSNR and coding efficiency, which shows that the proposed method in this paper is significantly efficient and robust. Meanwhile, the paper analyzes the coding speed of several modules in a specific way.更多还原