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A stable feedback control of the buffer state using the controlled Lagrange multiplier method.

J Choi1, D Park

  • 1Multimedia Lab., DACOM Corp. Res. and Dev. Center, Daejon.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
Summary
This summary is machine-generated.

A new stable feedback control algorithm for buffer-constrained adaptive quantization uses a controlled Lagrange multiplier. This algorithm offers easy implementation and low computational complexity with performance comparable to optimal methods.

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Area of Science:

  • Digital signal processing
  • Control theory
  • Information theory

Background:

  • Adaptive quantization is crucial for efficient data compression.
  • Buffer constraints in adaptive quantization systems can lead to performance degradation.
  • Existing control algorithms may suffer from high computational complexity.

Purpose of the Study:

  • To develop a stable feedback control algorithm for buffer-constrained adaptive quantization.
  • To analyze the stability of the proposed algorithm using Lyapunov stability theory.
  • To evaluate the performance and complexity of the proposed algorithm.

Main Methods:

  • Utilized feedback control theory to establish the algorithm.
  • Incorporated a controlled Lagrange multiplier within rate-distortion characteristics.
  • Employed Lyapunov stability theory to prove algorithm stability.
  • Analyzed the influence of average distortion-rate curves and buffer size on stability.

Main Results:

  • The proposed algorithm demonstrates stability, dependent on average distortion-rate curves and buffer size.
  • Sufficient conditions for algorithm stability were derived.
  • Experimental results show performance comparable to optimal algorithms.
  • The algorithm exhibits low computational complexity and ease of implementation.

Conclusions:

  • The proposed feedback control algorithm effectively manages buffer states in adaptive quantization.
  • It provides a practical solution with low complexity, balancing performance and implementation ease.
  • The stability analysis offers valuable insights for system design.