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Error-resilient pyramid vector quantization for image compression.

A C Hung1, E K Tsern, T H Meng

  • 1InterVideo Inc., Fremont, CA 94539, USA. achung@cs.stanford.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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Pyramid vector quantization (PVQ) offers robust image compression for noisy channels. A new indexing method enhances channel robustness by up to 3 dB, outperforming JPEG with greater error resilience.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Pyramid vector quantization (PVQ) is a fixed-rate compression technique using lattice points.
  • PVQ approximates optimal entropy-coded scalar quantizers for Laplacian-like sources.
  • Fixed-rate quantization offers inherent resilience to bit-error corruption in noisy channels.

Purpose of the Study:

  • Investigate Pyramid vector quantization (PVQ) for compressed image transmission over noisy channels.
  • Propose a novel indexing method for PVQ lattice points.
  • Enhance channel noise immunity for PVQ and general symmetric lattice quantizers.

Main Methods:

  • Developed a new method for deriving indices of multidimensional pyramid lattice points.
  • Applied PVQ for compressed image transmission over simulated noisy channels.

Related Experiment Videos

  • Evaluated performance against standard Joint Photographic Experts Group (JPEG) implementations.

    • Main Results:

    • The proposed indexing scheme improves channel robustness by up to 3 dB compared to previous methods.
    • The new PVQ algorithm demonstrates superior performance to typical JPEG implementations.
    • The fixed-rate coding algorithm exhibits significantly greater error resilience.

    Conclusions:

    • The novel PVQ indexing method enhances channel robustness for image transmission.
    • PVQ provides a viable and resilient alternative to JPEG for noisy environments.
    • The technique offers improved error resilience without significant computational overhead.