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

Robust rate-control for wavelet-based image coding via conditional probability models.

Matthew D Gaubatz1, Sheila S Hemami

  • 1Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 16, 2007
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

Field-Portable Microplastic Sensing in Aqueous Environments: A Perspective on Emerging Techniques.

Sensors (Basel, Switzerland)·2021
Same author

A computational intelligibility model for assessment and compression of American sign language video.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2011
Same author

Estimating the usefulness of distorted natural images using an image contour degradation measure.

Journal of the Optical Society of America. A, Optics, image science, and vision·2011
Same author

Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs.

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

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms.

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

VSNR: a wavelet-based visual signal-to-noise ratio for natural images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2007
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

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

This study introduces a fast, accurate method for wavelet image coding rate-control. The novel approach precisely estimates rate-quantization (R-Q) curves, achieving low percentage errors for efficient image compression.

Area of Science:

  • Digital image processing
  • Signal processing
  • Computer vision

Background:

  • Real-time rate-control is crucial for wavelet image coding.
  • Accurate characterization of rate-quantization (R-Q) curves is essential for effective rate-control.
  • Existing methods often lack robustness or speed.

Purpose of the Study:

  • To develop a robust and efficient method for characterizing R-Q curves in wavelet image coding.
  • To enable precise rate-control across various wavelet coders and quantization schemes.
  • To improve the accuracy and speed of rate-control for image compression.

Main Methods:

  • The proposed method uses two coder invocations to estimate R-Q curve slopes via probability modeling.
  • It employs a fast approximation to spatially adaptive probability modeling.

Related Experiment Videos

  • The approach is robust to different probability models, quantization schemes, and wavelet coders.

    • Main Results:

    • Achieved average percentage errors around 0.5% and 1.0% in achieving target rates.
    • Demonstrated comparable speed to state-of-the-art methods.
    • Outperformed traditional 2-coding-pass rho-domain modeling and post-compression rate-distortion optimization in certain aspects.

    Conclusions:

    • The presented method offers a fast, accurate, and robust solution for real-time rate-control in wavelet image coding.
    • It provides precise control over wavelet step sizes without requiring prior training.
    • This enhances the flexibility and efficiency of wavelet-based image compression systems.