Jove
Visualize
Contact Us

Related Experiment Videos

Hierarchical image coding via cerebellar model arithmetic computers.

Y Iiguni1

  • 1Dept. of Commun. Eng., Osaka Univ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
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

Image interpolation for progressive transmission by using radial basis function networks.

IEEE transactions on neural networks·2008
Same author

Progressive cross-section display of 3D medical images.

Medical & biological engineering & computing·2000
Same author

A nonlinear regulator design in the presence of system uncertainties using multilayered neural network.

IEEE transactions on neural networks·1991
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

This study introduces a novel hierarchical coding system using Cerebellar Model Arithmetic Computer (CMAC) for progressive image transmission. This method enhances image reconstruction quality and enables lossless transmission without filtering.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Hierarchical coding methods for progressive image transmission often rely on filtering techniques.
  • Conventional methods can suffer from blocking effects and complex computations.

Purpose of the Study:

  • To develop a novel hierarchical coding system for progressive image transmission.
  • To leverage the generalization and learning capabilities of CMAC for improved image reconstruction.
  • To achieve efficient image compression and lossless transmission.

Main Methods:

  • A hierarchical coding system employing Cerebellar Model Arithmetic Computer (CMAC) with varying generalization regions.
  • CMACs with wider regions learn low-frequency components; narrower regions handle finer details.

Related Experiment Videos

  • Compression achieved by reducing training signals and widening quantization intervals.

    • Main Results:

    • The proposed CMAC-based method avoids filtering, eliminating blocking effects.
    • Computation is simplified, excluding multiplication.
    • Fast reconstruction of the coarsest image and lossless progressive transmission are achieved.
    • Transmitted data equals original pixel count; reconstructed images match original size.

    Conclusions:

    • The CMAC-based hierarchical coding system offers an efficient and effective approach to progressive image transmission.
    • The method provides advantages over conventional techniques, including reduced computation and improved image quality.
    • Lossless progressive image transmission is achievable by accounting for quantization errors across levels.