Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Design of vector quantizer for image compression using self-organizing feature map and surface fitting.

Arijit Laha1, Nikhil R Pal, Bhabatosh Chanda

  • 1National Institute of Management, Calcutta 700 027, India. arijitl@yahoo.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 7, 2004
PubMed
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 author

Efficient High-Dimensional Learning With Adaptive Gaussian RBF Networks.

IEEE transactions on neural networks and learning systems·2025
Same author

Leader-Following Consensus of Time-Scale-Type Heterogeneous Nonlinear MASs via Periodic Event-Triggered Control.

IEEE transactions on cybernetics·2025
Same author

Event-Triggered Finite-Time Stabilization of Delayed T-S Fuzzy Systems on Time Scales.

IEEE transactions on cybernetics·2025
Same author

Boosting Drug-Disease Association Prediction for Drug Repositioning via Dual-Feature Extraction and Cross-Dual-Domain Decoding.

Journal of chemical information and modeling·2025
Same author

Unsupervised Feature Selection for High-Order Embedding Learning and Sparse Learning.

IEEE transactions on cybernetics·2025
Same author

Bi-Level Spectral Feature Selection.

IEEE transactions on neural networks and learning systems·2024
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
Same journal

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study introduces a novel vector quantization scheme for image compression, utilizing self-organizing feature maps and cubic surface modeling to enhance perceptual quality and achieve high compression ratios at low bit rates.

Area of Science:

  • Computer Vision
  • Signal Processing
  • Data Compression

Background:

  • Vector quantization (VQ) is a widely used technique for image compression.
  • Traditional VQ methods often struggle with balancing compression efficiency and perceptual fidelity.
  • Improving the psychovisual quality of reconstructed images at low bit rates remains a challenge.

Purpose of the Study:

  • To propose a new vector quantizer design for efficient image compression.
  • To enhance the perceptual fidelity of reconstructed images using cubic surface modeling.
  • To achieve better compression ratios while maintaining high image quality.

Main Methods:

  • Utilizing the self-organizing feature map (SOFM) algorithm to generate codevectors.
  • Modeling image blocks associated with codevectors using cubic surfaces.

Related Experiment Videos

  • Employing Huffman coding for indices and difference-coded mean values for compression.
  • Developing quantitative indices for assessing psychovisual quality (blocking effect).

    • Main Results:

    • The proposed scheme successfully generates a generic codebook from training images.
    • Cubic surface modeling improves perceptual fidelity, reducing blocking artifacts.
    • Combined Huffman coding and difference-coded means achieve significant compression ratios.
    • Experimental results show good quality reconstructed images at low bit rates.

    Conclusions:

    • The proposed vector quantization scheme offers an effective approach to image compression.
    • The method balances high compression efficiency with excellent perceptual quality.
    • It provides a valuable tool for applications requiring low bit rate image transmission and storage.