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Next-state functions for finite-state vector quantization.

N M Nasrabadi1, S A Rizvi

  • 1Dept. of Electr. and Comput. Eng., State Univ. of New York, Buffalo, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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Dynamic finite-state vector quantization (DFSVQ) improves subcodebook construction by evaluating various reordering procedures. Vector prediction-based reordering offers the best performance for DFSVQ encoding.

Area of Science:

  • Digital Signal Processing
  • Data Compression
  • Information Theory

Background:

  • Dynamic finite-state vector quantization (DFSVQ) is a compression technique utilizing subcodebooks derived from a larger supercodebook.
  • The efficiency of DFSVQ heavily relies on the method used to construct these subcodebooks.

Purpose of the Study:

  • To investigate and compare various reordering procedures for constructing DFSVQ subcodebooks.
  • To identify the most effective reordering strategy for enhancing DFSVQ performance.

Main Methods:

  • Developed and evaluated several reordering procedures for subcodebook construction.
  • Procedures investigated include conditional histogram, index prediction, vector prediction, nearest neighbor design, and frequency usage.
  • Performance metrics were hit ratio and computational complexity.

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Main Results:

  • The reordering procedure based on vector prediction demonstrated superior performance compared to other methods.
  • Vector prediction achieved a higher hit ratio, indicating more efficient subcodebook encoding.
  • Computational complexity was also considered in the performance evaluation.

Conclusions:

  • Vector prediction is the optimal reordering strategy for dynamic finite-state vector quantization subcodebook construction.
  • This finding can lead to more efficient data compression algorithms using DFSVQ.