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research article

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Ascenso, Joao
•
Alshina, Elena
•
Ebrahimi, Touradj  
January 1, 2023
Ieee Multimedia

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union - Telecommunication Sector (ITU-T) for the development of the first image coding standard based on machine learning (a subset of artificial intelligence), offering a single stream, compact compressed domain representation, targeting both human visualization and machine consumption. The main motivation for this upcoming standard is the excellent performance of tools based on deep neural networks, in image coding, computer vision, and image processing tasks. The JPEG AI aims to develop an image coding standard addressing the needs of a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data, and media distribution. This article presents and discusses the rationale behind the JPEG AI vision, notably how this new standardization initiative aims to shape the future of image coding, through relevant application-driven use cases. The JPEG AI requirements, the JPEG AI history, and current status are also presented, offering a glimpse of the development of the first learning-based image coding standard.

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Type
research article
DOI
10.1109/MMUL.2023.3245919
Web of Science ID

WOS:000988304600011

Author(s)
Ascenso, Joao
•
Alshina, Elena
•
Ebrahimi, Touradj  
Date Issued

2023-01-01

Publisher

IEEE COMPUTER SOC

Published in
Ieee Multimedia
Volume

30

Issue

1

Start page

100

End page

111

Subjects

Computer Science, Hardware & Architecture

•

Computer Science, Information Systems

•

Computer Science, Software Engineering

•

Computer Science, Theory & Methods

•

Computer Science

•

performance evaluation

•

visualization

•

image coding

•

artificial intelligence

•

surveillance

•

transform coding

•

streaming media

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
MMSPL  
Available on Infoscience
June 19, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/198426
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