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Generalized threshold replenishment: an adaptive vector quantization algorithm for the coding of nonstationary

J E Fowler1

  • 1Dept. of Electr. Eng., Ohio State Univ., Columbus, OH 43210, USA. fowler@ece.msstate.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 16, 2008
PubMed
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A new adaptive-vector-quantization (AVQ) algorithm, generalized threshold replenishment (GTR), offers superior low-rate coding performance for non-stationary sources. GTR

Area of Science:

  • Digital signal processing
  • Image and video compression
  • Machine learning for data compression

Background:

  • Traditional adaptive-vector-quantization (AVQ) algorithms often rely on batch training, limiting real-time applicability.
  • Prior AVQ methods may not explicitly optimize for both rate and distortion simultaneously during coding.
  • Non-stationary data sources present unique challenges for efficient compression algorithms.

Purpose of the Study:

  • Introduce a novel AVQ algorithm, generalized threshold replenishment (GTR), for enhanced coding of non-stationary sources.
  • Address limitations of existing AVQ algorithms by incorporating online rate-distortion considerations.
  • Achieve superior rate-distortion performance, especially at low bit rates.

Main Methods:

  • Developed the generalized threshold replenishment (GTR) algorithm, an adaptive-vector-quantization (AVQ) technique.

Related Experiment Videos

  • Implemented online consideration of both rate and distortion in GTR's codeword selection and codebook updates.
  • Evaluated GTR's performance on an image sequence and an artificial non-stationary random process.

    • Main Results:

    • GTR demonstrates superior rate-distortion performance compared to other AVQ algorithms, particularly for low-rate coding.
    • For an image sequence, GTR achieved a coding rate below 1.0 b/pixel, outperforming other AVQ methods.
    • Most AVQ algorithms significantly reduced distortion compared to non-adaptive VQ at approximately 1.5 b/pixel.

    Conclusions:

    • The GTR algorithm offers a significant advancement in adaptive-vector-quantization for non-stationary data.
    • GTR's online nature makes it suitable for real-time hardware and software implementations.
    • The proposed algorithm achieves state-of-the-art low-rate performance in data compression.