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Optimal trellis-based buffered compression and fast approximations.

A Ortega1, K Ramchandran, M Vetterli

  • 1Dept. of Electr. Eng., Columbia Univ., New York, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1994
PubMed
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Researchers developed optimal and fast approximate solutions for buffer-constrained adaptive quantization problems. These methods optimize signal coding in limited buffer environments, offering benchmarks for buffer control strategies.

Area of Science:

  • Information Theory
  • Computer Science
  • Signal Processing

Background:

  • Adaptive quantization is crucial for efficient data compression in systems with limited buffer capacity.
  • Nonstationary signal sequences present challenges for traditional fixed quantization methods.
  • Buffer-constrained environments necessitate specialized algorithms to balance distortion and buffer size.

Purpose of the Study:

  • To formalize the buffer-constrained adaptive quantization problem for discrete nonstationary signals.
  • To derive both optimal and computationally efficient suboptimal solutions.
  • To establish a benchmark for evaluating buffer control strategies in data compression.

Main Methods:

  • Formulation as a constrained, discrete optimization problem, linked to integer programming.

Related Experiment Videos

  • Application of forward dynamic programming with the Viterbi algorithm for optimal solutions.
  • Development of a heuristic algorithm using Lagrangian optimization and an operational rate-distortion framework.

    • Main Results:

    • An optimal solution using dynamic programming and a faster, near-optimal heuristic algorithm were developed.
    • The heuristic algorithm achieves performance close to optimal with significantly reduced computational complexity.
    • The derived solutions are applicable to any additive, globally minimum distortion criterion.

    Conclusions:

    • The study provides a robust framework for buffer-constrained adaptive quantization.
    • The developed algorithms offer practical solutions for applications like video encoding and multimedia displays.
    • These methods enable efficient data compression while minimizing buffer requirements.