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Related Experiment Videos

Optimization of packetization masks for image coding based on an objective cost function for desired packet

Joost Rombaut1, Aleksandra Pizurica, Wilfried Philips

  • 1Department for Telecommunications and Information Processing, Ghent University, Ghent, Belgium. joost.rombaut@telin.ugent.be

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 12, 2008
PubMed
Summary

This study introduces a new packetization method to improve image transmission over lossy networks. The technique enhances image quality and viewer experience by minimizing quality fluctuations during packet loss.

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Area of Science:

  • Digital image processing
  • Network communication protocols
  • Error resilience in multimedia transmission

Background:

  • Packet loss in image communication over lossy networks causes image degradation.
  • Passive error concealment methods use neighboring data to reconstruct damaged images.
  • Effective packetization is crucial for spreading image data and aiding reconstruction.

Purpose of the Study:

  • To develop a novel robust packetization method for image transmission over lossy packet networks.
  • To define criteria for optimal packetization and propose a cost function for packetization masks.
  • To improve the quality and viewer experience of reconstructed images under packet loss.

Main Methods:

  • Defined novel criteria for effective packetization.
  • Proposed a cost function for packetization masks based on defined criteria.
  • Utilized stochastic optimization to compute optimal packetization masks.
  • Evaluated the technique on wavelet and DCT coding schemes.

Main Results:

  • Achieved comparable or superior mean image quality compared to existing methods.
  • Significantly reduced quality fluctuations, enhancing viewer experience.
  • Considerably improved worst-case image quality, particularly at high packet loss rates.

Conclusions:

  • The proposed packetization method offers robust image transmission over lossy networks.
  • It enhances visual quality and viewer satisfaction by minimizing reconstruction artifacts.
  • This technique is particularly beneficial for applications sensitive to image quality variations.