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Related Experiment Videos

Variable-length constrained-storage tree-structured vector quantization.

U Bayazit1, W A Pearlman

  • 1Toshiba American Electronic Components, Inc., San Jose, CA 95131, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces Variable-Length Constrained Storage Tree-Structured Vector Quantization (VLCS-TSVQ), an algorithm that achieves coding performance close to existing methods while significantly reducing codebook size. This enables efficient transmission of code vector probabilities for adaptive entropy coding.

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Area of Science:

  • Digital signal processing
  • Information theory
  • Data compression

Background:

  • Constrained Storage Vector Quantization (CSVQ) offers low complexity but balanced codebooks.
  • Variable-Length Tree-Structured Vector Quantization (VLTSVQ) provides superior coding performance due to nonuniform rate distribution.

Purpose of the Study:

  • To develop a Variable-Length Constrained Storage Tree-Structured Vector Quantization (VLCS-TSVQ) algorithm.
  • To achieve high coding performance with reduced codebook storage complexity.

Main Methods:

  • Utilizes codebook sharing from CSVQ.
  • Greedily grows an unbalanced tree-structured residual vector quantizer.
  • Applies constrained storage principles.

Main Results:

  • VLCS-TSVQ performance closely approaches greedy growth VLTSVQ.
  • Codebook storage complexity scales linearly with the rate.
  • Demonstrated effectiveness on 1-D synthetic sources and real-world images.

Conclusions:

  • VLCS-TSVQ offers a practical approach to high-performance vector quantization with reduced storage.
  • The reduced codebook size facilitates efficient side information transmission for adaptive entropy coding.