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

TL;DR

This paper introduces an importance-aware waterfilling framework for real-time computer vision data transmission, optimizing power allocation based on data importance to improve reconstruction quality under bandwidth constraints.

Contribution

It proposes a novel importance-weighted MSE metric and a data-importance-aware waterfilling method for adaptive power allocation in real-time CV applications.

Findings

Achieves over 7 dB gain in normalized IMSE at high SNRs

Outperforms margin-adaptive waterfilling and equal power strategies

Enhances data efficiency and robustness in resource-constrained environments

Abstract

This paper presents a novel framework for importance-aware adaptive data transmission, designed specifically for real-time computer vision (CV) applications where task-specific 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 ($> 10$ dB), 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.