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

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.