Analysis and Transcoding Time Prediction of Online Videos

2015 IEEE International Symposium on Multimedia (ISM) Pub Date : 2015-12-01 DOI:10.1109/ISM.2015.100

Tewodros Deneke, S. Lafond, J. Lilius

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引用次数: 6

Abstract

Today, video content is delivered to a myriad of devices over different communication networks. Video delivery must be adapted to the available bandwidth, screen size, resolution and the decoding capability of the end user devices. In this work we present an approach to predict the transcoding time of a video into another given transcoding parameters and an input video. To obtain enough information on the characteristics of real world online videos and their transcoding parameters needed to model transcoding time, we built a video characteristics dataset, using data collected from a large video-on-demand system, YouTube. The dataset contains a million randomly sampled video instances listing 10 fundamental video characteristics. We report our analysis on the dataset which provides insightful statistics on fundamental online video characteristics that can be further exploited to optimize or model components of a multimedia processing systems. We also present experimental results on transcoding time prediction models, based on support vector machines, linear regression and multi-layer perceptron feed forward artificial neural network.

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在线视频的转码时间预测与分析

今天，视频内容通过不同的通信网络传送到无数的设备上。视频传输必须适应终端用户设备的可用带宽、屏幕尺寸、分辨率和解码能力。在这项工作中，我们提出了一种方法来预测视频到另一个给定的转码参数和输入视频的转码时间。为了获得足够的关于真实世界在线视频特征及其转码时间模型所需的转码参数的信息，我们建立了一个视频特征数据集，使用从大型视频点播系统YouTube收集的数据。该数据集包含一百万个随机采样的视频实例，列出了10个基本视频特征。我们报告了我们对数据集的分析，该数据集提供了关于基本在线视频特征的有见地的统计数据，可以进一步利用这些数据集来优化或建模多媒体处理系统的组件。本文还介绍了基于支持向量机、线性回归和多层感知机前馈人工神经网络的转码时间预测模型的实验结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Symposium on Multimedia (ISM)

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