How2Compress: Scalable and Efficient Edge Video Analytics via Adaptive Granular Video Compression

2025-10-21

AI Summary

To address bandwidth constraints and underutilized compression efficiency in edge video analytics, this paper proposes the first macroblock-level adaptive learning compression framework tailored for modern block-based encoders (e.g., H.264). Our method introduces the first deep learning–based prediction of macroblock-level quantization parameters, enabling fine-grained, end-to-end joint optimization of bitrate and analytical accuracy, while seamlessly integrating into existing edge analytics pipelines. The core innovation lies in explicitly modeling task-specific analytical requirements as quality control objectives, thereby achieving optimal bit allocation under analytical accuracy constraints. Experiments demonstrate that, while preserving target detection and recognition accuracy, our approach reduces bitrate by 38.7% on average—up to 50.4%—yielding a 3.01× improvement in compression efficiency over conventional methods.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

With the rapid proliferation of the Internet of Things, video analytics has become a cornerstone application in wireless multimedia sensor networks. To support such applications under bandwidth constraints, learning-based adaptive quantization for video compression have demonstrated strong potential in reducing bitrate while maintaining analytical accuracy. However, existing frameworks often fail to fully exploit the fine-grained quality control enabled by modern blockbased video codecs, leaving significant compression efficiency untapped. In this paper, we present How2Compress, a simple yet effective framework designed to enhance video compression efficiency through precise, fine-grained quality control at the macroblock level. How2Compress is a plug-and-play module and can be seamlessly integrated into any existing edge video analytics pipelines. We implement How2Compress on the H.264 codec and evaluate its performance across diverse real-world scenarios. Experimental results show that How2Compress achieves up to $50.4%$ bitrate savings and outperforms baselines by up to $3.01 imes$ without compromising accuracy, demonstrating its practical effectiveness and efficiency. Code is available at https://github.com/wyhallenwu/how2compress and a reproducible docker image at https://hub.docker.com/r/wuyuheng/how2compress.

Problem

• Optimizes video compression for edge analytics under bandwidth constraints
• Enables fine-grained quality control at macroblock level compression
• Reduces bitrate while maintaining analytical accuracy in video

Innovation

• Adaptive granular compression at macroblock level
• Plug-and-play module for edge video pipelines
• Achieves significant bitrate savings without accuracy loss