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

Optimization and implementation of the integer wavelet transform for image coding.

Marco Grangetto1, Enrico Magli, Maurizio Martina

  • 1Dipt. di Elettronica, Politecnico di Torino, Torino, Italy. grangetto@polito.it

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
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

L-Dopa/Carbidopa intestinal gel infusion in advanced Parkinson's disease: real-life mobility insights from wearable sensors.

Frontiers in neurology·2026
Same author

Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality.

Sleep medicine reviews·2026
Same author

From RGB-D to RGB-Only: Reliability and Clinical Relevance of Markerless Skeletal Tracking for Postural Assessment in Parkinson's Disease.

Sensors (Basel, Switzerland)·2026
Same author

A multi-level annotated sensor dataset of gait freezing manifestations and severity in Parkinson's disease.

Scientific data·2026
Same author

A Wavelet- and Machine Learning-Based Framework for the Automatic Detection of Artefacts in Electromyography REM sleep.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Spike Train Scalograms (STS): a Deep Learning Classification Pipeline for Neuronal Cell Types.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
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 an integer wavelet transform (IWT) for image compression. Optimized IWT designs achieve excellent lossless and lossy compression, with minimal performance impact from finite precision coefficients.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • VLSI Design

Background:

  • Wavelet transforms are crucial for image compression.
  • Integer Wavelet Transform (IWT) offers advantages in lossless and lossy compression.
  • Optimizing IWT implementation is key for efficient hardware design.

Purpose of the Study:

  • To design and implement an image transform coding algorithm using IWT.
  • To propose criteria for optimal wavelet filter factorization within the lifting scheme.
  • To analyze the impact of finite precision on IWT compression performance and propose a VLSI architecture.

Main Methods:

  • Development of criteria for optimal wavelet filter polyphase matrix factorization.
  • Implementation of IWT using the lifting scheme.

Related Experiment Videos

  • Analysis of finite precision effects on lifting coefficients.
  • Design of a VLSI architecture for IWT.

    • Main Results:

    • IWT implementations demonstrate satisfactory lossless and lossy compression performance.
    • Limited mantissa bits for lifting coefficients result in minimal performance degradation.
    • Proposed VLSI architecture achieves high frame rates with moderate gate complexity.

    Conclusions:

    • Optimal factorization and finite precision analysis lead to efficient IWT designs.
    • The proposed VLSI architecture is suitable for high-speed image compression applications.
    • The research contributes to the advancement of hardware-accelerated image transform coding.