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

A stochastic framework for rate-distortion optimized video coding over error-prone networks.

Oztan Harmanci1, A Murat Tekalp

  • 1University of Rochester, Rochester, NY 14627, USA. harmanci@ece.rochester.edu

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

Padé Neurons for Efficient Neural Models.

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

End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
Same author

Realizing a Low-Power Head-Mounted Phase-Only Holographic Display by Light-Weight Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
Same author

Two-dimensional mesh-based mosaic representation for manipulation of video objects with occlusion.

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

Temporal segmentation of video objects for hierarchical object-based motion description.

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

Robust color histogram descriptors for video segment retrieval and identification.

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

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

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

This study introduces a stochastic framework for video encoding over unreliable networks. It optimizes video quality by considering packet loss, outperforming existing error control methods.

Area of Science:

  • Video coding and transmission
  • Information theory
  • Signal processing

Background:

  • Video codecs are susceptible to packet loss in error-prone networks.
  • Existing methods struggle to optimize Rate-Distortion (RD) performance under varying channel conditions.
  • Mean Square Error (MSE) is a common distortion measure, but its accurate estimation is challenging in stochastic environments.

Purpose of the Study:

  • To develop a comprehensive stochastic framework for Rate-Distortion (RD) optimal encoder design for video transmission over error-prone networks.
  • To enable optimal encoder design for any motion-compensated predictive video codec.
  • To improve video quality and transmission efficiency in the presence of packet loss.

Main Methods:

  • The framework utilizes Mean Square Error (MSE) as the distortion measure, considering an ensemble of channels and packet loss probability.

Related Experiment Videos

  • It involves computing the expected value and second moment of reference frames, termed the stochastic frame buffer.
  • A recursive procedure is proposed for calculating these stochastic frame buffer components.

    • Main Results:

    • Optimal motion-compensated prediction in the MSE sense requires the expected value of reference frames.
    • MSE calculation necessitates the computation of the second moment of reference frames.
    • The proposed stochastic RD optimization method effectively selects optimal macroblock modes and motion vectors based on packet loss probability.

    Conclusions:

    • The developed stochastic framework provides a robust method for RD optimal encoder design over error-prone networks.
    • The framework can incorporate channel feedback but does not require a lossless or even existing feedback channel.
    • Experimental results demonstrate superior performance compared to existing error tracking and control schemes, particularly for applications like multicast streaming.