Search research articles

Search research articles

Related Experiment Videos

Variable-branch tree-structured vector quantization.

Shiueng-Bien Yang¹

¹Department of Computer Science and Information Engineering, Leader University, Tainan City, Taiwan, ROC. ysb@mail.leader.edu.tw

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

|September 29, 2004

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a variable-branch tree-structured vector quantizer (VBTSVQ) using genetic algorithms. VBTSVQ improves coding efficiency and codeword accuracy compared to traditional fixed-branch TSVQ methods.

Area of Science:

Computer Science
Signal Processing
Machine Learning

Background:

Traditional tree-structured vector quantizers (TSVQ) use fixed M-ary trees, artificially dividing data into a set number of clusters per node.
A key limitation of TSVQ is that the selected codeword may not be the closest match to the input vector, impacting coding efficiency.
Existing TSVQ variants often struggle with optimal cluster representation and codeword accuracy.

Purpose of the Study:

To propose a novel Variable-Branch Tree-Structured Vector Quantizer (VBTSVQ) that optimizes tree structure.
To enhance codeword accuracy by addressing the limitation of non-closest codeword selection in conventional TSVQs.
To introduce a multiclassification encoding method for improved cluster representation within VBTSVQ.

Main Methods:

Related Experiment Videos

Development of a genetic algorithm to dynamically determine the optimal number of child nodes for each splitting node.
Implementation of a multiclassification encoding strategy to represent clusters with multiple components.
Comparative analysis of VBTSVQ against existing TSVQ methods through experimental evaluation.

Main Results:

VBTSVQ demonstrates superior performance over other TSVQ variants in experimental tests.
The proposed multiclassification encoding ensures that VBTSVQ codewords closely match full search results.
The genetic algorithm effectively optimizes the variable-branch structure for improved vector quantization.

Conclusions:

VBTSVQ offers a significant advancement over fixed-branch TSVQs by enabling adaptive tree structures.
The combination of genetic algorithms and multiclassification encoding enhances coding efficiency and accuracy.
VBTSVQ presents a more effective approach to vector quantization, particularly in scenarios demanding high fidelity.