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Multiple description wavelet based image coding.

S D Servetto1, K Ramchandran, V A Vaishampayan

  • 1Laboratoire de Communications Audiovisuelles, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland. servetto@lcavsun1.epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 8, 2008
PubMed
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This study introduces novel image coding algorithms for reliable data transmission over unreliable networks. The new methods achieve comparable image quality at a significantly lower bit rate, enhancing data transmission efficiency.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Transmitting images over error-prone channels (e.g., packet networks, wireless systems) faces challenges due to data loss.
  • Existing robust image coding methods often require high bit rates for reliable transmission.
  • Transient channel shutdowns and deep fades lead to packet loss, impacting received image fidelity.

Purpose of the Study:

  • To develop advanced image coding algorithms resilient to packet loss in error-prone communication environments.
  • To improve the efficiency of robust image coding by reducing bit rate requirements while maintaining quality.
  • To offer greater flexibility in managing redundancy allocation across multiple data descriptions.

Main Methods:

  • The proposed algorithms combine multiple description scalar quantizers (MDSQ) with efficient subband coding techniques.

Related Experiment Videos

  • Images are encoded into multiple independent packets, ensuring quality depends on the number of received packets, not their specific identities.
  • The approach focuses on designing robust quantizers and efficient packetization strategies for image data.

    • Main Results:

    • The developed image coders achieve comparable Peak Signal-to-Noise Ratio (PSNR) values to existing state-of-the-art methods on standard test images.
    • These new coders typically require 50-60% of the bit rate compared to previously reported robust image coders.
    • The method provides enhanced freedom in allocating redundancy across different data descriptions.

    Conclusions:

    • The proposed image coding scheme offers a significant improvement in efficiency for transmitting images over lossy channels.
    • The algorithms provide a robust and flexible solution for robust image transmission, particularly in packet-switched and wireless networks.
    • This work advances the field of image compression for unreliable communication systems by balancing quality, bit rate, and redundancy management.