On the impact of learning-based image compression on computer vision tasks

Shunsuke Akamatsu; Michela Testolina; Evgeniy Upenik; Touradj Ebrahimi

doi:10.1117/12.3030885

30 September 2024 On the impact of learning-based image compression on computer vision tasks

Shunsuke Akamatsu, Michela Testolina, Evgeniy Upenik, Touradj Ebrahimi

Proceedings Volume 13137, Applications of Digital Image Processing XLVII; 131370M (2024) https://doi.org/10.1117/12.3030885
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. While most of the research to date has focused on image coding for humans, JPEG AI plans to address both human and machine vision by presenting several non-normative decoders addressing multiple image processing and computer vision tasks in addition to standard reconstruction. While the impact of conventional image compression on computer vision tasks has already been addressed, no study has been conducted to assess the impact of learning-based image compression on such tasks. In this paper, the impact of learning-based image compression, including JPEG AI, on computer vision tasks is reviewed and discussed, mainly focusing on the image classification task along with object detection and segmentation. This study reviews the impact of image compression with JPEG AI on various computer vision models. It shows the superiority of JPEG AI over other conventional and learning-based compression models.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shunsuke Akamatsu, Michela Testolina, Evgeniy Upenik, and Touradj Ebrahimi "On the impact of learning-based image compression on computer vision tasks", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 131370M (30 September 2024); https://doi.org/10.1117/12.3030885

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