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Vector Representation of Complex Numbers01:16

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Related Experiment Video

Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Vector quantization for entropy coding of image subbands.

T Senoo1, B Girod

  • 1Media Lab., MIT, Cambridge, MA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

Entropy-constrained vector quantization for image subband coding is explored. Lattice quantizers offer near-optimal performance, providing an efficient alternative to computationally intensive methods for image compression.

Related Experiment Videos

Last Updated: Jul 7, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Digital image processing
  • Information theory
  • Data compression

Background:

  • Entropy coding is crucial for efficient image compression.
  • Vector quantization (VQ) is a powerful technique for entropy coding.
  • Image subbands require specialized VQ approaches for optimal compression.

Purpose of the Study:

  • To investigate vector quantization for entropy coding of image subbands.
  • To evaluate the performance of different VQ methods, including lattice quantizers.
  • To determine the most effective and computationally feasible approach for image subband compression.

Main Methods:

  • Computed rate-distortion curves using mean square error.
  • Implemented and compared full-search entropy-constrained vector quantization (ECVQ).
  • Evaluated lattice quantizers, including orthogonal lattice quantizers.
  • Applied an optimal bit allocation rule using Lagrange multipliers.

Main Results:

  • Full-search ECVQ provides the best performance but is computationally demanding.
  • Lattice quantizers achieve coding efficiency nearly indistinguishable from optimal ECVQ.
  • Orthogonal lattice quantizers perform comparably to lattice quantizers from dense sphere packings.
  • The Lagrange multiplier-based bit allocation rule is effective for subband coding.

Conclusions:

  • Lattice quantizers represent a highly efficient and practical solution for entropy coding of image subbands.
  • These findings offer a balance between compression performance and computational complexity.
  • The study demonstrates the effectiveness of optimized bit allocation in subband coding schemes.