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

Video compression using spatiotemporal regularity flow.

Orkun Alatas1, Omar Javed, Mubarak Shah

  • 1School of Electrical Engineering and Computer Science at the University of Central Florida, Orlando, FL 32816, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 13, 2006
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

Negative pressure intrusion cementation versus tourniquet use in total knee arthroplasty: optimising cement mantle thickness.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie·2026
Same author

STROKEVISION-BENCH: A MULTIMODAL VIDEO AND 2D POSE BENCHMARK FOR TRACKING STROKE RECOVERY.

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing·2026
Same author

Leveraging sparse annotations for leukemia diagnosis on the large leukemia dataset.

Medical image analysis·2025
Same author

DiffAct++: Diffusion Action Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Foundation Models Defining a New Era in Vision: A Survey and Outlook.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Rebuttal to "Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features' ".

IEEE transactions on pattern analysis and machine intelligence·2024
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

This study introduces a novel wavelet video coding framework that enhances compression rates by leveraging spatiotemporal data regularity. The spatiotemporal regularity flow (SPREF) method significantly improves video compression efficiency.

Area of Science:

  • Digital Signal Processing
  • Image and Video Compression
  • Computer Vision

Background:

  • Video compression relies on exploiting data redundancy.
  • Wavelet-based methods offer efficient compression but can be further optimized.
  • Spatiotemporal data in video sequences exhibits inherent regularity.

Purpose of the Study:

  • To develop a new wavelet video coding framework to improve compression rates.
  • To exploit spatiotemporal data regularity for enhanced compression efficiency.
  • To introduce a novel representation for spatiotemporal regularity directions.

Main Methods:

  • A spatiotemporal volume is analyzed to identify directions of minimal pixel variation (regularity).
  • Spatiotemporal Regularity Flow (SPREF) is proposed, using splines to represent these regularity directions.

Related Experiment Videos

  • Directional decomposition is performed using 3-D orthonormal bandelet basis.

    • Main Results:

    • The proposed method achieves higher compression rates compared to standard wavelet-based compression.
    • SPREF effectively removes temporal redundancy and compensates for spatial redundancy.
    • The spline representation of SPREF offers low storage overhead.

    Conclusions:

    • The SPREF-based video compression framework significantly enhances compression performance.
    • Exploiting spatiotemporal regularity is crucial for advanced video coding.
    • The method offers a promising approach for efficient video compression applications.