面向计算机视觉应用中自适应语义通信的数据重要性感知功率分配
Data-Importance-Aware Power Allocation for Adaptive Semantic Communication in Computer Vision Applications
AI总结：
针对实时计算机视觉应用任务特定保真度需求，提出数据重要性感知自适应传输框架，引入IMSE指标并设计注水功率分配方法，结合子像素与语义段重要性模型，在高信噪比下归一化IMSE增益超基线7-10dB，提升数据效率与鲁棒性。
摘要：
沉浸式扩展现实等变革生活的应用正在革新无线通信和计算机视觉（CV）领域。本文针对任务特定保真度至关重要的实时CV应用，提出一种新颖的重要性感知自适应数据传输框架。引入一种新的重要性加权均方误差（IMSE）指标作为面向任务的重建质量度量，该指标考虑了子像素级重要性（SP-I）和语义段级重要性（SS-I）模型。为在总功率约束下最小化IMSE，提出数据重要性感知注水方法，根据数据重要性和信道条件优化分配传输功率，优先处理高重要性子流。仿真结果表明，所提方法显著优于边际自适应注水和等功率分配策略。结合SP-I和SS-I模型的数据划分实现了最显著的改进，在高信噪比（>10 dB）下，归一化IMSE增益超过基线7 dB和10 dB。这些显著增益凸显了所提框架在提升实时CV应用数据效率和鲁棒性方面的潜力，尤其在带宽受限和资源紧张环境中。
英文摘要：
Life-transformative applications such as immersive extended reality are revolutionizing wireless communications and computer vision (CV). This paper presents a novel framework for importance-aware adaptive data transmissions, designed specifically for real-time CV applications where task-specific fidelity is critical. A novel importance-weighted mean square error (IMSE) metric is introduced as a task-oriented measure of reconstruction quality, considering sub-pixel-level importance (SP-I) and semantic segment-level importance (SS-I) models. To minimize IMSE under total power constraints, data-importance-aware waterfilling approaches are proposed to optimally allocate transmission power according to data importance and channel conditions, prioritizing sub-streams with high importance. Simulation results demonstrate that the proposed approaches significantly outperform margin-adaptive waterfilling and equal power allocation strategies. The data partitioning that combines both SP-I and SS-I models is shown to achieve the most significant improvements, with normalized IMSE gains exceeding $7\,$dB and $10\,$dB over the baselines at high SNRs ($>10\,$dB). These substantial gains highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
