Modern video codecs have been extensively optimized to preserve perceptual quality, leveraging models of the human visual system. However, in split inference systems-where intermediate features from neural network are transmitted instead of pixel data-these assumptions no longer apply. Intermediate features are abstract, sparse, and task-specific, making perceptual fidelity irrelevant. In this paper, we investigate the use of Versatile Video Coding (VVC) for compressing such features under the MPEG-AI Feature Coding for Machines (FCM) standard. We perform a tool-level analysis to understand the impact of individual coding components on compression efficiency and downstream vision task accuracy. Based on these insights, we propose three lightweight essential VVC profiles-Fast, Faster, and Fastest. The Fast profile provides 2.96% BD-Rate gain while reducing encoding time by 21.8%. Faster achieves a 1.85% BD-Rate gain with a 51.5% speedup. Fastest reduces encoding time by 95.6% with only a 1.71% loss in BD-Rate.
@article{arxiv.2512.08227,
title = {New VVC profiles targeting Feature Coding for Machines},
author = {Md Eimran Hossain Eimon and Ashan Perera and Juan Merlos and Velibor Adzic and Hari Kalva},
journal= {arXiv preprint arXiv:2512.08227},
year = {2026}
}
Comments
Accepted for presentation at ICIP 2025 workshop on Coding for Machines