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A fast search algorithm for vector quantization using L2-norm pyramid of codewords.

Byung Cheol Song1, Jong Beom Ra

  • 1Department of Electrical Engineering and Computer Science, Korea Advanced Institute of Science and Technology, Taejon 305-701, Korea.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
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This study introduces a fast algorithm for vector quantization (VQ) image compression, significantly reducing encoding time. The method speeds up codeword search without compromising compression quality.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Vector quantization (VQ) is crucial for image compression.
  • VQ encoding is computationally intensive due to the exhaustive search for the nearest codeword.

Purpose of the Study:

  • To develop a fast algorithm for accelerating the closest codeword search in VQ encoding.
  • To reduce the computational complexity of VQ encoding while maintaining image quality.

Main Methods:

  • The algorithm utilizes a topological codebook structure to derive an elimination condition.
  • This condition prunes unnecessary matching operations during the search process.

Main Results:

  • The proposed algorithm significantly reduces encoding complexity with minimal preprocessing and memory overhead.

Related Experiment Videos

  • Encoding quality is preserved, matching that of full search algorithms.
  • The algorithm demonstrates superior performance compared to existing search methods.

    • Conclusions:

    • A novel, efficient algorithm for VQ image compression has been developed.
    • The algorithm offers a practical solution for reducing VQ encoding time without quality degradation.
    • This approach presents a significant improvement over current VQ search techniques.