2024/07/24 by Weijun Wang, Wang, Weijun, Liang Mi +11 · 1 citation
Computer Science · #Advanced Data Compression Techniques #FOS: Computer and information sciences #Image Enhancement Techniques #Networking and Internet Architecture (cs.NI) #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2407.16990
openalex publication_date 2024/07/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Video analytics is widespread in various applications serving our society. Recent advances of content enhancement in video analytics offer significant benefits for the bandwidth saving and accuracy improvement. However, existing content-enhanced video analytics systems are excessively computationally expensive and provide extremely low throughput. In this paper, we present region-based content enhancement, that enhances only the important regions in videos, to improve analytical accuracy. Our system, RegenHance, enables high-accuracy and high-throughput video analytics at the edge by 1) a macroblock-based region importance predictor that identifies the important regions fast and precisely, 2) a region-aware enhancer that stitches sparsely distributed regions into dense tensors and enhances them efficiently, and 3) a profile-based execution planer that allocates appropriate resources for enhancement and analytics components. We prototype RegenHance on five heterogeneous edge devices. Experiments on two analytical tasks reveal that region-based enhancement improves the overall accuracy of 10-19% and achieves 2-3x throughput compared to the state-of-the-art frame-based enhancement methods.