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Related Experiment Videos

Fast planar-oriented ripple search algorithm for hyperspace VQ codebook.

Chin-Chen Chang1, Wen-Chuan Wu

  • 1Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 621, Taiwan, ROC. ccc@cs.ccu.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 6, 2007
PubMed
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This study introduces a faster codebook search for vector quantization (VQ), reducing complexity. The novel method achieves near full-search VQ performance with improved speed and minimal extra storage.

Area of Science:

  • Computer Science
  • Signal Processing
  • Data Compression

Background:

  • Vector Quantization (VQ) is crucial for data compression but suffers from high computational complexity, particularly in the codebook search phase.
  • Full-search VQ offers optimal performance but is computationally prohibitive for many real-time applications.
  • Existing partial-search methods offer speed improvements but often at the cost of significant quantization distortion.

Purpose of the Study:

  • To develop a fast and efficient codebook search algorithm for Vector Quantization.
  • To reduce the computational complexity associated with the quantization process in VQ.
  • To achieve quantization performance close to full-search VQ while maintaining high speed.

Main Methods:

  • The proposed method utilizes a planar Voronoi diagram to define a ripple search domain, enabling localized searching.

Related Experiment Videos

  • A duplication mechanism is incorporated to minimize quantization distortion, approaching the accuracy of full-search VQ.
  • The algorithm focuses on searching local regions rather than performing a global exploration of the codebook.

    • Main Results:

    • Experimental results demonstrate a significant increase in quantization speed compared to existing partial-search methods.
    • The proposed method achieves quantization distortion levels comparable to full-search VQ.
    • The algorithm requires only a minimal amount of additional storage for the duplication mechanism.

    Conclusions:

    • The developed fast codebook search method effectively reduces VQ complexity.
    • The approach offers a favorable trade-off between speed, accuracy, and storage requirements.
    • This method presents a viable solution for efficient VQ implementation in resource-constrained environments.