A framework for advanced video traces: evaluating visual quality for video transmission over lossy networks

  • Authors:

  • Osama A. Lotfallah;Martin Reisslein;Sethuraman Panchanathan

  • Affiliations:

  • Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Department of Electrical Engineering, Arizona State University, Tempe, AZ;Department of Computer Science and Engineering, Arizona State University, Tempe, AZ

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2006

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Abstract

Conventional video traces (which characterize the video encoding frame sizes in bits and frame quality in PSNR) are limited to evaluating loss-free video transmission. To evaluate robust video transmission schemes for lossy network transport, generally experiments with actual video are required. To circumvent the need for experiments with actual videos, we propose in this paper an advanced video trace framework. The two main components of this framework are (i) advanced video traces which combine the conventional video traces with a parsimonious set of visual content descriptors, and (ii) quality prediction schemes that based on the visual content descriptors provide an accurate prediction of the quality of the reconstructed video after lossy network transport. We conduct extensive evaluations using a perceptual video quality metric as well as the PSNR in which we compare the visual quality predicted based on the advanced video traces with the visual quality determined from experiments with actual video. We find that the advanced video trace methodology accurately predicts the quality of the reconstructed video after frame losses.