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Research Domains
Information And Computing Sciences
Computer Vision And Multimedia Computation
Video Processing
Learned Scalable Video Coding For Humans And Machines.

Home
Research Domains
Information And Computing Sciences
Computer Vision And Multimedia Computation
Video Processing
Learned Scalable Video Coding For Humans And Machines.

Related Experiment Video

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Published on: December 3, 2013

Learned scalable video coding for humans and machines.

Hadi Hadizadeh¹, Ivan V Bajić¹

¹School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6 Canada.

EURASIP Journal on Image and Video Processing

|November 18, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

A new video coding method supports machine vision tasks and human viewing. This end-to-end learned codec offers efficient video compression for both machine analytics and human review, outperforming existing methods.

Keywords:

Coding for machines Deep learning Scalable coding Video analytics

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Area of Science:

Computer Vision and Machine Learning
Video Compression and Signal Processing

Background:

Traditional video coding focuses on human viewing, but deep neural networks (DNNs) enable machine vision applications.
Existing video codecs are not optimized for dual use in machine analytics and human review, necessitating a new approach.
Applications like traffic monitoring require continuous machine analysis with occasional human oversight.

Purpose of the Study:

To introduce an end-to-end learnable video codec designed for both machine vision and human viewing.
To enable efficient, scalable video representation and compression for dual-purpose applications.
To improve video analytics performance while maintaining quality for human perception.

Main Methods:

Developed an end-to-end learnable video codec with a base layer for machine vision tasks.

Video compression

Incorporated an enhancement layer for human viewing reconstruction, utilizing conditional coding principles.

Evaluated the framework on four standard video datasets.

Main Results:

The proposed codec's base layer significantly outperforms state-of-the-art learned and conventional codecs for machine vision tasks.
The enhancement layer, combined with the base layer, achieves comparable performance to existing codecs for human viewing.
Conditional coding principles contributed to enhanced compression gains.

Conclusions:

The developed learnable video codec effectively supports machine vision tasks and human viewing in a scalable manner.
This dual-purpose codec represents a significant advancement for applications requiring both automated analysis and human review.
The framework demonstrates superior efficiency for machine vision while maintaining quality for human perception.