嵌入式系统 Nvidia Jetson 平台上所有 Kvazaar 和 x265 HEVC 编码器的性能评估

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
R. James, Mohammed Abo-Zahhad, Koji Inoue, Mohammed S. Sayed
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引用次数: 0

摘要

对高质量视频日益增长的需求需要复杂的编码技术,这不仅耗费资源,还增加了编码时间,给嵌入式系统的实时处理带来了挑战。Kvazaar 和 x265 编码器是高效视频编码(HEVC)标准的两种高效实现。本文使用两种编码配置(高速预设和高质量预设)评估了 Nvidia Jetson 平台上 All Intra Kvazaar 和 x265 编码器的性能。在我们的工作中,我们使用了两种情况:第一种情况是在 CPU 上运行这两种编码器,根据平均编码时间,Kvazaar 比 x265 快 65.44% 和 69.4%,在高速预置和高质量预置下,比 x265 的 BD 速率分别提高了 1.88% 和 0.6%。在第二种情况下,两个编码器在 Nvidia Jetson 的 GPU 上运行,结果显示在每个预设值下的平均编码时间比基于 CPU 的情况缩短了一半。此外,在高速和高质量预设下,Kvazaar 比 x265 分别快 54.5% 和 56.70%,BD 速率分别提高 1.93% 和 0.45%。在可扩展性方面,CPU 上的两个编码器可线性扩展至四个线程,之后速度保持不变。在 GPU 上,两个编码器随线程数线性扩展。所获得的结果证实,Kvazaar 编码器更高效,与 x265 HEVC 编码器相比,它具有更高的速度和性能,可用于嵌入式系统的实时视频应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance evaluation of all intra Kvazaar and x265 HEVC encoders on embedded system Nvidia Jetson platform

The growing demand for high-quality video requires complex coding techniques that cost resource consumption and increase encoding time which represents a challenge for real-time processing on Embedded Systems. Kvazaar and x265 encoders are two efficient implementations of the High-Efficient Video Coding (HEVC) standard. In this paper, the performance of All Intra Kvazaar and x265 encoders on the Nvidia Jetson platform was evaluated using two coding configurations; highspeed preset and high-quality preset. In our work, we used two scenarios, first, the two encoders were run on the CPU, and based on the average encoding time Kvazaar proved to be 65.44% and 69.4% faster than x265 with 1.88% and 0.6% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. In the second scenario, the two encoders were run on the GPU of the Nvidia Jetson, and the results show the average encoding time under each preset is reduced by half of the CPU-based scenario. In addition, Kvazaar is 54.5% and 56.70% faster with 1.93% and 0.45% BD-rate improvement over x265 at high-speed and high-quality preset, respectively. Regarding the scalability, the two encoders on the CPU are linearly scaled up to four threads and speed remains constant afterward. On the GPU, the two encoders are scaled linearly with the number of threads. The obtained results confirmed that, Kvazaar is more efficient and that it can be used on Embedded Systems for real-time video applications due to its high speed and performance over the x265 HEVC encoder

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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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