Semantic Scholar Open Access 2023 102 sitasi

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

J. Ascenso Elena Alshina T. Ebrahimi

Abstrak

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.

Topik & Kata Kunci

Penulis (3)

J

J. Ascenso

E

Elena Alshina

T

T. Ebrahimi

Format Sitasi

Ascenso, J., Alshina, E., Ebrahimi, T. (2023). The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption. https://doi.org/10.1109/MMUL.2023.3245919

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Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Total Sitasi
102×
Sumber Database
Semantic Scholar
DOI
10.1109/MMUL.2023.3245919
Akses
Open Access ✓