Region-based Content Enhancement for Efficient Video Analytics at the Edge

Learn about a novel approach to video analytics optimization in this 14-minute conference presentation from NSDI '25. Discover how researchers from Tsinghua University, Nanjing University, and University of Göttingen developed RegenHance, a system that enhances only important regions in videos rather than entire frames to achieve more efficient edge computing performance. Explore the three key components of their solution: a macroblock-based region importance predictor for fast and precise identification of critical areas, a region-aware enhancer that efficiently processes sparsely distributed regions by stitching them into dense tensors, and a profile-based execution planner for optimal resource allocation between enhancement and analytics components. Examine experimental results demonstrating 10-19% accuracy improvements and 2-3× throughput gains compared to traditional frame-based enhancement methods across five heterogeneous edge devices and two analytical tasks. Understand how this region-based content enhancement approach addresses the computational expense and low throughput limitations of existing content-enhanced video analytics systems while maintaining high accuracy for bandwidth-constrained edge environments.