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Related Experiment Video

Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Successive refinement lattice vector quantization.

Debargha Mukherjee¹, Sanjit K Mitra

¹Dept. of Electr. and Comput. Eng., Univ. of California, Santa Barbara, CA 93106, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 6, 2008

Summary

This summary is machine-generated.

This study introduces Voronoi lattice vector quantization (VLVQ) for efficient, progressive vector quantization. VLVQ enables successive refinement of vector data, offering advantages for applications like wavelet image coding.

Related Experiment Videos

Last Updated: Jul 7, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Digital Signal Processing
Information Theory
Image Compression

Background:

Lattice Vector Quantization (LVQ) offers general codebooks but struggles with high-resolution vector quantization indices for successive refinement.
Existing methods lack a unified framework for progressive uniform vector quantization while retaining lattice quantization's mean-squared-error benefits.

Purpose of the Study:

Develop a unified framework for progressive uniform vector quantization.
Introduce Voronoi lattice VQs (VLVQ) for efficient successive refinement.
Achieve the vector counterpart of scalar bitplane-wise refinement.

Main Methods:

Developed a successive refinement uniform vector quantization methodology using lattice codebooks shaped by previous stage Voronoi regions (VLVQ).
Introduced efficiency measures based on index entropy for VLVQs.
Created a constructive method for asymptotically optimal uniform quantization using tree-structured subset VLVQs and entropy coding.

Main Results:

VLVQ provides a successive refinement methodology for vector quantization.
Developed methods for measuring VLVQ efficiency and achieving asymptotic optimality.
Applied VLVQ to refine wavelet coefficients within the Vector Set-Partitioning in Hierarchical Trees (VSPIHT) framework.

Conclusions:

VLVQ offers a promising approach for progressive vector quantization, analogous to scalar bitplane refinement.
While asymptotic optimality benefits are challenging in practice, VLVQ shows potential for wavelet image coding.
VLVQ techniques were successfully applied to VSPIHT, with results comparable to existing methods.