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An efficient encoding algorithm for vector quantization based on subvector technique.

Jeng-Shyang Pan1, Zhe-Ming Lu, Sheng-He Sun

  • 1Dept. of Electron. Eng., Nat. Kaohsiung Univ. of Appl. Sci., Taiwan. jspan@cc.kuas.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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A new vector quantization encoding algorithm utilizes vector sum and variance for faster processing. This method significantly reduces computation time and distortion calculations compared to existing algorithms.

Area of Science:

  • Computer Science
  • Signal Processing
  • Data Compression

Background:

  • Vector quantization (VQ) is a widely used technique in data compression and signal processing.
  • Traditional VQ encoding algorithms often suffer from high computational complexity, limiting their real-time applications.
  • Existing fast search algorithms like ENNS and EENNS offer improvements but can be further optimized.

Purpose of the Study:

  • To introduce a novel and efficient encoding algorithm for vector quantization.
  • To leverage vector characteristics (sum and variance) for accelerated codeword search.
  • To reduce computational time and distortion calculations without compromising accuracy.

Main Methods:

  • The proposed algorithm divides vectors into two subvectors.

Related Experiment Videos

  • It employs three inequalities based on sums and variances to prune non-optimal codewords.
  • The method compares its performance against Equal-Average Nearest Neighbor Search (ENNS) and Equal-Average Equal-Variance Nearest Neighbor Search (EENNS) variants.

    • Main Results:

    • The algorithm demonstrates superior speed compared to ENNS, improved ENNS, EENNS, and improved EENNS.
    • It achieves a 2.4% to 6% reduction in computational time and a 20.5% to 26.8% decrease in distortion calculations versus improved EENNS.
    • Average improvements of 4% in computational time and 24.6% in distortion calculations were observed for codebook sizes from 128 to 1024.

    Conclusions:

    • The proposed encoding algorithm offers a significant speedup for vector quantization.
    • Its reliance on vector sum and variance properties provides an effective method for codeword rejection.
    • This algorithm presents a computationally efficient alternative for VQ applications requiring fast encoding.