Data-Importance-Aware Waterfilling for Adaptive Real-Time Communication in Computer Vision Applications

This paper presents a novel framework for importance-aware adaptive data transmission, designed specifically for real-time computer vision (CV) applications where taskspecific fidelity is critical. An importance-weighted mean square error (IMSE) metric is introduced, assigning data importance based on bit positions within pixels and semantic relevance within visual segments, thus providing a task-oriented measure of reconstruction quality. To minimize IMSE under the total power constraint, a data-importance-aware waterfilling approach is proposed to optimally allocate transmission power according to data importance and channel conditions. Simulation results demonstrate that the proposed approach significantly outperforms margin-adaptive waterfilling and equal power allocation strategies, achieving more than 7 dB and 10 dB gains in normalized IMSE at high SNRs (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;10 ~\text{dB}$</tex>), respectively. These results 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.