Scene Change Detection-Based Discrete Autoregressive Modeling for MPEG-4 Video Traffic

Irene Spanou; Aggelos Lazaris; Polychronis Koutsakis

doi:10.1109/ICC.2013.6654888

Scene Change Detection-Based Discrete Autoregressive Modeling for MPEG-4 Video Traffic

Irene Spanou, Aggelos Lazaris, Polychronis Koutsakis

In Proc. of the IEEE ICC, Budapest, Hungary, June 2013

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Overview

A hybrid traffic model for MPEG-4 video that pairs a lightweight DAR(1) model with scene-change detection, for much better accuracy on bursty content.

Key contributions

Studies 21 videos across genres such as lectures, cartoons, talk shows, action and sports.
Two-state Markov chain that switches between high- and low-activity scenes on top of DAR(1).
Keeps the model cheap (four traffic parameters) while improving accuracy considerably.

High- and low-activity scenes, each modeled with DAR(1), with a two-state Markov chain switching between them.

Abstract

With more than 16 billion videos streamed on YouTube during last May and recent estimates by Cisco that mobile video traffic will increase 25-fold between 2011 and 2016, there is a pressing need to adequately serve large numbers of simultaneous online video transmissions. Network providers need to be able to guarantee the strict Quality-of-Service (QoS) requirements of real-time variable bit rate (VBR) video users, and a good statistical model for multiplexed video traffic can help significantly to evaluate and enhance network performance under various video loads. In this paper, we propose and evaluate a new hybrid video traffic model for MPEG-4 video. The genres of the videos considered in our study include lectures, cartoons, talk shows, action movies, sci-fi and sports. In the first part of our work, we build a Discrete Autoregressive model of order one (DAR(1)) and discuss its efficiency on capturing the behavior of real MPEG-4 video traces. We then proceed to build and evaluate a hybrid model which combines the DAR(1) model with a scene-change detection and classification algorithm, in order to provide us with higher modeling accuracy. The classification is performed based on the average number of bits generated during the scenes and the scene activity is modeled by a Markov chain where each state represents the degree of activity (high/low). Our extensive experimental evaluation study shows that the proposed hybrid method significantly improves the efficiency of pure DAR(1) schemes.

Cite this paper

@inproceedings{DBLP:conf/icc/SpanouLK13,
  author    = {Irene Spanou and
               Aggelos Lazaris and
               Polychronis Koutsakis},
  title     = {Scene change detection-based Discrete Autoregressive modeling for
               {MPEG-4} video traffic},
  booktitle = {Proceedings of {IEEE} International Conference on Communications,
               {ICC} 2013, Budapest, Hungary, June 9-13, 2013},
  pages     = {2386--2390},
  year      = {2013},
  crossref  = {DBLP:conf/icc/2013},
  url       = {https://doi.org/10.1109/ICC.2013.6654888},
  doi       = {10.1109/ICC.2013.6654888},
  timestamp = {Fri, 27 Dec 2019 00:00:00 +0100},
  biburl    = {https://dblp.org/rec/bib/conf/icc/SpanouLK13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}