Pre-fetching based on video analysis for interactive region-of-interest streaming of soccer sequences

  • Authors:

  • Aditya Mavlankar;Bernd Girod

  • Affiliations:

  • Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA;Information Systems Laboratory, Department of Electrical Engineering, Stanford University, Stanford, CA

  • Venue:
  • ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
  • Year:
  • 2009

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Abstract

We consider a video streaming system in which the user can interactively watch an arbitrary region of a high-spatial-resolution scene. Region-of-interest (RoI) prediction helps pre-fetch select slices of encoded video. The more accurate the RoI prediction the lower is the percentage of missing pixels. We compare different techniques for RoI prediction for streaming soccer sequences. Two techniques proposed in our earlier work are not domain-specific and can be applied to any type of content. Here we propose two techniques geared for soccer sequences that perform semantic video analysis. The goal of the paper is to find out whether domain-specific techniques can predict the client's RoI more accurately. Experiments indicate that for short prediction look-ahead there is little gain whereas for a long prediction look-ahead of 2 seconds the percentage of missing pixels can be reduced from 24% for the best general technique to 18% for the best domain-specific technique. This translates to a PSNR gain of around 1 dB for long prediction look-ahead. The percentage of missing pixels can be reduced further by spending additional bitrate for pre-fetching a margin around the predicted RoI.