Wang Wei, Jin Xiaoping, Quan Daying, Zhu Mingmin, Wang Xiaofeng, Zheng Ming, Li Jingjian, Chen Jianhua
College of Information Engineering The Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, China Jiliang University, Hangzhou, 310018, China.
School of Computer and Information, Anhui Normal University, Wuhu, 241002, China.
Sci Rep. 2024 Nov 29;14(1):29666. doi: 10.1038/s41598-024-81603-8.
Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates and data transmission lowers sensor node costs and energy, addressing communication bottlenecks. Block compressed sampling (BCS) can meet the above requirements. For BCS, the sparsity or smoothness of the block signal is a crucial parameter, which determines the setting range of the sampling rate. For the sampling side of the sensor node, we cannot directly obtain the complete digital signal. Therefore, it becomes difficult to perform adaptive rate compressed sampling. In this paper, a novel adaptive sampling rate allocation scheme based on region division is proposed. First, we use a simple auxiliary vector to determine the complex and smooth regions of the current image. For the smooth region, we use a mean vector to divide each block into a residual block and a mean value block. Then the proposed prior probability sparsity estimation model is used to estimate the sparsity order of each residual block, while each mean value block requires only one measurement to restore losslessly. For the complex region, we first set a higher baseline sampling rate for it, and then adaptively allocate the remaining supplementary sampling rate based on the statistical characteristics of each block itself. Experiment results show that the proposed scheme can allocate an appropriate sampling rate to each block, reduce the total sampling rate, and significantly improve the signal reconstruction quality simultaneously.
无线传感器网络（WSN）中的图像采集与传输是一些资源匮乏的多媒体传感应用的核心问题。降低采样率和数据传输量可降低传感器节点成本与能耗，解决通信瓶颈问题。块压缩采样（BCS）能够满足上述要求。对于BCS而言，块信号的稀疏性或平滑性是一个关键参数，它决定了采样率的设置范围。对于传感器节点的采样端，我们无法直接获取完整的数字信号。因此，进行自适应速率压缩采样变得困难。本文提出了一种基于区域划分的新型自适应采样率分配方案。首先，我们使用一个简单的辅助向量来确定当前图像的复杂区域和平滑区域。对于平滑区域，我们使用均值向量将每个块划分为一个残差块和一个均值块。然后，使用所提出的先验概率稀疏性估计模型来估计每个残差块的稀疏度阶数，而每个均值块仅需一次测量即可无损恢复。对于复杂区域，我们首先为其设置一个较高的基线采样率，然后根据每个块自身的统计特性自适应地分配剩余的补充采样率。实验结果表明，所提出的方案能够为每个块分配合适的采样率，降低总采样率，同时显著提高信号重建质量。
