摘要
在新媒体视频业务快速发展的今天,传统单机视频转码能力已经出现瓶颈.在Hadoop云计算平台的研究基础上,结合当前主流的音视频处理工具FFmpeg,提出了一种新的视频转码方案.该方案通过使用Hadoop两大核心:HDFS(Hadoop Distributed File System)和Map Reduce编程思想,进行分布式转码.同时,还详细地介绍和设计了分布式转码的具体流程.最后实验结果表明,该分布式转码方案在效率上有较大提高.在实验中,视频的分段大小也影响着视频转码的时间.随着分段大小从小到大,同样的视频转码时间变化却是由高降低再升高.从实验数据来看,相对于其他的分段,分段大小为32M的时候,转码时间最佳.
With the rapid development of new media video services today, traditional standalone video transcoding capability has been a bottleneck. This paper proposes a new video transcoding scheme based on the research on Hadoop cloud computing platform and the current mainstream audio and video processing tool FFmpeg. This scheme fulfills distributed transcoding method by using two core components in Hadoop： HDFS（Hadoop Distributed File System） and the programming ideas of Map Reduce. Meanwhile, the paper also describes in detail the specific processes and design of distributed transcoding. Finally, experimental results show that The distributed transcoding scheme has greatly improved the efficiency. Simultaneously in the experiment, the segment size of each video file also impacts the time of video transcoding process. The experiment shows that as the segment size goes from small to large, time consumed by the transcoding process index experiences a curve： firstly it changes from large to small, then drops to its lowest point and gradually rises. The lowest point exists when the segment size is adjusted to 32 M.
出处
《计算机系统应用》
2016年第8期54-60,共7页
Computer Systems & Applications
