Enabling Next-Generation Consumer Experience with Feature Coding for Machines
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909953421213696 |
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| author | Eimon, Md Eimran Hossain Merlos, Juan Perera, Ashan Kalva, Hari Adzic, Velibor Furht, Borko |
| author_facet | Eimon, Md Eimran Hossain Merlos, Juan Perera, Ashan Kalva, Hari Adzic, Velibor Furht, Borko |
| contents | As consumer devices become increasingly intelligent and interconnected, efficient data transfer solutions for machine tasks have become essential. This paper presents an overview of the latest Feature Coding for Machines (FCM) standard, part of MPEG-AI and developed by the Moving Picture Experts Group (MPEG). FCM supports AI-driven applications by enabling the efficient extraction, compression, and transmission of intermediate neural network features. By offloading computationally intensive operations to base servers with high computing resources, FCM allows low-powered devices to leverage large deep learning models. Experimental results indicate that the FCM standard maintains the same level of accuracy while reducing bitrate requirements by 75.90% compared to remote inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Next-Generation Consumer Experience with Feature Coding for Machines Eimon, Md Eimran Hossain Merlos, Juan Perera, Ashan Kalva, Hari Adzic, Velibor Furht, Borko Computer Vision and Pattern Recognition As consumer devices become increasingly intelligent and interconnected, efficient data transfer solutions for machine tasks have become essential. This paper presents an overview of the latest Feature Coding for Machines (FCM) standard, part of MPEG-AI and developed by the Moving Picture Experts Group (MPEG). FCM supports AI-driven applications by enabling the efficient extraction, compression, and transmission of intermediate neural network features. By offloading computationally intensive operations to base servers with high computing resources, FCM allows low-powered devices to leverage large deep learning models. Experimental results indicate that the FCM standard maintains the same level of accuracy while reducing bitrate requirements by 75.90% compared to remote inference. |
| title | Enabling Next-Generation Consumer Experience with Feature Coding for Machines |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.09232 |