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Related Experiment Videos

Fast full search equivalent encoding algorithms for image compression using vector quantization.

C M Huang1, Q Bi, G S Stiles

  • 1Dept. of Electr. Eng., Utah State Univ., Logan, UT.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
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Three new search routines for vector quantization (VQ) image compression offer significant speedups. These geometric algorithms match full search accuracy using substantially fewer computations than existing methods.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Vector Quantization (VQ) is a widely used image compression technique.
  • The encoding phase of VQ, particularly the search for the nearest codeword, is computationally intensive.
  • Existing search algorithms often involve a trade-off between speed and accuracy.

Purpose of the Study:

  • To develop novel, computationally efficient search routines for the VQ encoding phase.
  • To achieve results comparable to exhaustive search while reducing computational load.
  • To offer improved performance over recently proposed alternative search methods.

Main Methods:

  • Development of three fast search routines based on geometric considerations.
  • Implementation and testing of these routines within VQ image compression systems.

Related Experiment Videos

  • Comparison of computational operations against exhaustive search and other advanced algorithms.

    • Main Results:

    • The proposed geometric search routines yield identical results to an exhaustive search.
    • These algorithms require only 3-20% of the mathematical operations of a full search.
    • The routines outperform recent alternative algorithms, needing less than 50% of their operations.

    Conclusions:

    • The presented geometric search routines significantly enhance the efficiency of VQ image compression encoding.
    • These methods provide a practical solution for accelerating VQ without compromising compression quality.
    • The algorithms represent a notable advancement in fast search techniques for vector quantization.