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Related Experiment Video

Updated: May 5, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Exploring Learned Surveillance Video Coding with Long-Term Reference and Adaptive Long-Short Modeling.

Yuansheng Wu¹, Liangchao Hu², Xiaodan Song²

¹National Key Laboratory of Complex Aviation System Simulation, Southwest China Institute of Electronic Technology, Chengdu 610036, China.

Sensors (Basel, Switzerland)

|May 4, 2026

Summary

This summary is machine-generated.

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This study introduces a novel long-term reference (LTR) method for learned surveillance video coding, improving efficiency. The adaptive approach balances motion importance, achieving significant BD-rate savings compared to H.266/VVC.

Area of Science:

Computer Vision
Video Compression
Machine Learning

Background:

Efficient video transmission is crucial for surveillance cameras.
Long-term reference (LTR) in traditional video coding is well-studied, but its application in learned video coding remains underexplored.
Challenges include handling unequal motion importance and motion overhead in dense motion representations like optical flow.

Purpose of the Study:

To establish a baseline for LTR in learned surveillance video coding.
To propose an adaptive long-short modeling approach addressing challenges in learned video coding.
To enhance LTR quality and integrate it effectively within an end-to-end learned video coding framework.

Main Methods:

Introduced LTR and a long-short context mining module to the end-to-end video coding exploration model (EEM).

Keywords:

adaptive context mining adaptive motion modeling learned video coding long-term reference long–short correlation surveillance video coding

Related Experiment Videos

Last Updated: May 5, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Enhanced LTR quality and proposed a long-short motion adapter to manage unequal motion importance.

Implemented a historical motion guidance module to assist motion decoding.

Main Results:

Achieved 13.89% BD-rate savings in YUV-PSNR compared to H.266/VVC under a low-delay P configuration.
Improved upon a baseline of 1.86% BD-rate loss on EEM-4.1.
Demonstrated competitive performance with lower computational resource consumption compared to other methods.

Conclusions:

The proposed adaptive LTR approach effectively addresses challenges in learned surveillance video coding.
Integrating this LTR method with stronger learned video coding baselines is expected to yield further performance gains.
The method offers a promising direction for efficient learned video compression in surveillance applications.