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

Predictive residual vector quantization [image coding].

S A Rizvi1, N M Nasrabadi

  • 1Dept. of Electr. and Comput. Eng., State Univ. of New York, Buffalo, NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
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

A simple method to view stereo-images of the optic nerve head on ordinary computer monitors.

Eye (London, England)·2008
Same author

Maternal risk factors associated with low birth weight in Karachi: a case-control study.

Eastern Mediterranean health journal = La revue de sante de la Mediterranee orientale = al-Majallah al-sihhiyah li-sharq al-mutawassit·2008
Same author

Automatic target recognition using a feature-decomposition and data-decomposition modular neural network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

A modular neural network vector predictor for predictive image coding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Identifying and treating patients with suboptimal responses.

Neurology·2004
Same author

Management of pediatric urolithiasis in Pakistan: experience with 1,440 children.

The Journal of urology·2003
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

A novel predictive residual vector quantization (PRVQ) method enhances performance by combining predictive vector quantization (PVQ) and residual vector quantization (RVQ). This new technique achieves superior results with reduced computational complexity.

Area of Science:

  • Digital Signal Processing
  • Machine Learning
  • Data Compression

Background:

  • Vector quantization (VQ) is crucial for efficient data compression.
  • Predictive VQ (PVQ) and Residual VQ (RVQ) offer distinct advantages but have limitations.
  • Optimizing predictor and quantizer components jointly is challenging.

Purpose of the Study:

  • Introduce Predictive Residual Vector Quantization (PRVQ), a novel VQ technique.
  • Develop a joint optimization method for PRVQ components using neural networks.
  • Evaluate PRVQ performance against existing VQ methods.

Main Methods:

  • PRVQ combines a multilayer perceptron-based vector predictor and a competitive neural network-based RVQ.
  • A neural network learning algorithm facilitates nonlinear constrained optimization for joint codebook design.

Related Experiment Videos

  • Lagrangian formulation is used for iterative optimization of stage error functions.

    • Main Results:

    • PRVQ outperforms equivalent RVQ by 2 dB and unconstrained VQ by 1.7 dB at the same bit rate.
    • PRVQ demonstrates superior rate-distortion performance compared to PVQ.
    • The proposed method achieves significantly lower codebook search complexity.

    Conclusions:

    • PRVQ offers a high-performance VQ scheme with reduced search complexity.
    • The joint optimization technique effectively integrates predictor and quantizer design.
    • PRVQ represents a significant advancement in efficient data compression.