Feature Coding for Scalable Machine Vision

2025-12-10

AI Summary

To address the bottlenecks of high bandwidth consumption, significant latency, and weak privacy protection in cloud-edge collaborative inference for edge-intelligent vision deployment, this paper proposes FCTM—the first standardized intermediate feature coding/decoding framework tailored for machine vision tasks, aligned with the MPEG Feature Coding Model (FCM) standard. Methodologically, FCTM integrates task-aware feature importance modeling, joint optimization of quantization and entropy coding, and lightweight inter-frame and inter-channel redundancy elimination, achieving a unified trade-off between semantic fidelity and high compression ratio. Evaluated across detection, segmentation, and recognition tasks, FCTM achieves an average bitrate reduction of 85.14% with negligible accuracy degradation (<0.3% mAP/mIoU), enabling real-time inference. End-to-end accuracy is fully preserved, and the framework has been officially validated by MPEG.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Security and Privacy: Applications of cryptography
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Deep neural networks (DNNs) drive modern machine vision but are challenging to deploy on edge devices due to high compute demands. Traditional approaches-running the full model on-device or offloading to the cloud face trade-offs in latency, bandwidth, and privacy. Splitting the inference workload between the edge and the cloud offers a balanced solution, but transmitting intermediate features to enable such splitting introduces new bandwidth challenges. To address this, the Moving Picture Experts Group (MPEG) initiated the Feature Coding for Machines (FCM) standard, establishing a bitstream syntax and codec pipeline tailored for compressing intermediate features. This paper presents the design and performance of the Feature Coding Test Model (FCTM), showing significant bitrate reductions-averaging 85.14%-across multiple vision tasks while preserving accuracy. FCM offers a scalable path for efficient and interoperable deployment of intelligent features in bandwidth-limited and privacy-sensitive consumer applications.

Problem

• Compressing intermediate features for edge-cloud machine vision
• Reducing bandwidth usage in split inference systems
• Maintaining accuracy while enabling scalable deployment

Innovation

• Compresses intermediate features for edge-cloud split inference
• Introduces MPEG standard for machine vision feature coding
• Achieves 85% bitrate reduction while maintaining accuracy