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Entropy-constrained tree-structured vector quantizer design.

K Rose1, D Miller, A Gersho

  • 1Dept. of Electr. and Comput. Eng., California Univ., Santa Barbara, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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A new Leaf-Optimal Tree Design (LOTD) method improves tree-structured vector quantizers by addressing suboptimality issues. This approach offers guaranteed performance gains, achieving up to 1 dB distortion reduction in image coding compared to older methods.

Area of Science:

  • Signal Processing
  • Information Theory
  • Computer Vision

Background:

  • Existing tree-structured vector quantizer (TSVQ) design methods, like the generalized Breiman-Friedman-Olshen-Stone (GBFOS) algorithm, face limitations.
  • These limitations include greedy growing strategies, suboptimal encoding rules, and the necessity of time sharing for achieving desired compression rates.

Purpose of the Study:

  • To introduce a novel Leaf-Optimal Tree Design (LOTD) method for TSVQ.
  • To overcome the identified shortcomings of conventional TSVQ design algorithms.
  • To achieve performance improvements in data compression, particularly for image coding.

Main Methods:

  • The LOTD method reoptimizes tree structures from conventional designs with minimal complexity increase.
  • It incorporates an optimal entropy-constrained nearest-neighbor rule for encoding at the leaf nodes.

Related Experiment Videos

  • The method provides explicit quantizer solutions across all rates without requiring time sharing.

    • Main Results:

    • Theoretical guarantees for performance improvement are established.
    • Simulations for image coding show a reduction of approximately 1 dB in distortion for a given rate compared to the GBFOS method.
    • The LOTD method demonstrates superior efficiency and effectiveness in TSVQ design.

    Conclusions:

    • The LOTD method represents a significant advancement in TSVQ design.
    • It offers enhanced performance and efficiency over existing algorithms.
    • This technique is particularly beneficial for applications like image compression where distortion reduction is critical.