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

Joao Ascenso, Elena Alshina, Touradj Ebrahimi, Dan Grois

    Research output: Contribution to journalArticlepeer-review

    53 Scopus citations

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)100-111
    Number of pages12
    JournalIEEE Multimedia
    Volume30
    Issue number1
    DOIs
    StatePublished - 1 Jan 2023

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Media Technology
    • Hardware and Architecture
    • Computer Science Applications

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