Visible light communication (VLC) has emerged as a promising technology for wireless communication due to its advantages of the vast optical spectrum, high energy efficiency, and no electromagnetic interference radiation. With the widespread adoption of LED infrastructure and camera-equipped smart devices, optical camera communication (OCC) has gained momentum as a pragmatic version of VLC based on commercial off-the-shelf (COTS) devices. Compared with VLC systems based on photodiodes (PD), the information-carrying capability of OCC enables it to provide a wide range of services in the areas of intelligent transportation, indoor positioning, underwater communication, and the Internet of Things (IoT). This paper presents a brief overview of the OCC system, focuses on the constraints affecting OCC performance, and offers feasible solutions for dependable data transmission in complex and diverse scenarios. Finally, this paper summarizes the potential extended applications of OCC, hoping to push this advanced form of optical wireless communication toward practical deployments in our daily lives.

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.