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

Rate-distortion optimal video summary generation.

Zhu Li1, Guido M Schuster, Aggelos K Katsaggelos

  • 1Multimedia Research Laboratory (MRL), Motorola Laboratories, Schaumburg, IL 60196, USA. zhu.li@motorola.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 22, 2005
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

Investigating Structurally and Pigmentary Colored Featherworks via Noninvasive Methodologies.

ACS omega·2026
Same author

Optimizing atrial fibrillation detection through ECG feature selection using Extra-Trees and statistical association measures.

Journal of electrocardiology·2026
Same author

Predicting substance use behaviors with machine learning using small sets of judgment and contextual variables.

Npj mental health research·2026
Same author

Automated HFrEF Diagnosis Using an Optimized TimeSformer Model in Echocardiography.

Journal of imaging informatics in medicine·2025
Same author

Comprehensive Optoelectronic Study of Copper Nitride: Dielectric Function and Bandgap Energies.

Nanomaterials (Basel, Switzerland)·2025
Same author

Using Variational Autoencoders for Out of Distribution Detection in Histological Multiple Instance Learning.

IEEE access : practical innovations, open solutions·2025
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

Video summarization creates concise versions of videos to save viewing time and resources. This study optimizes summarization by balancing conciseness and distortion using a rate-distortion framework.

Area of Science:

  • Computer Science
  • Multimedia Processing
  • Information Theory

Background:

  • Video summarization is crucial for managing large video datasets and limited viewing time.
  • Applications requiring reduced storage, bandwidth, or power also benefit from shorter video sequences.
  • Video summarization inherently involves a trade-off between conciseness and introduced distortion.

Purpose of the Study:

  • To address the need for efficient video summarization.
  • To develop a novel metric for quantifying summarization distortion.
  • To formulate video summarization as a rate-distortion optimization problem.

Main Methods:

  • Defined summarization rate as the ratio of original frames (m) to summary frames (n).
  • Developed a new distortion metric for video summarization.

Related Experiment Videos

  • Formulated the problem as a rate-distortion optimization.
  • Employed dynamic programming for optimal algorithm development.
  • Considered practical constraints, such as maximum frame skipping.

    • Main Results:

    • Optimal dynamic programming algorithms were developed for video summarization.
    • Experimental comparisons were made between optimal and heuristic algorithms.
    • The proposed rate-distortion framework effectively balances conciseness and distortion.

    Conclusions:

    • Dynamic programming provides an optimal solution for video summarization under rate-distortion constraints.
    • The developed framework and algorithms offer a systematic approach to video summarization.
    • Consideration of practical constraints enhances the applicability of the summarization methods.