Southampton Business School, University of Southampton, Southampton SO17 1BJ, UK.
College of Computer Science, Sichuan University, Chengdu 610065, China.
Sensors (Basel). 2022 Nov 1;22(21):8375. doi: 10.3390/s22218375.
Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.
基于智能手机上的发光二极管 (LED) 和嵌入式摄像头的光通信 (OCC) 由于 LED 照明和移动设备的普及而引起了相当多的关注。然而，大多数现有研究并没有考虑到在解码过程中感兴趣区域 (ROI) 提取的性能瓶颈，因此进一步提高通信容量具有挑战性。为此，我们提出了一种基于像素灰度值的快速网格虚拟划分方案，该方案在不牺牲计算复杂度的情况下快速提取 ROI，从而减少解码延迟并提高 OCC 的通信容量。本质上，所提出的方案使用网格划分策略将接收到的图像划分为块，并在不同的块中随机采样几个像素，以快速定位原始图像中具有高灰度值的 ROI。通过实现轻量级 ROI 提取算法，我们在实验中验证了其在降低解码延迟方面的有效性，表明其在通信容量方面具有优越的性能。实验结果清楚地表明，在传输频率为 5 kHz 时，与 iPhone 接收器的高斯模糊方案相比，所提出方案的解码延迟降低了 70%。
