## 1 Introduction  
As important components of underground infrastructure, pipelines are designed for collecting and transporting resources such as oil, gas, water, etc. However, the pipeline network is vulnerable to losing its functionality due to internal corrosion, cracking, deformation, and manufacturing flaws.  
Around the world, accidents and damage resulting from pipeline leakage are often reported by the media. If the accident happens with a water pipeline, it is a waste of resources and may cause a slight environmental pollution [1]. However, if a pipeline which transports oil, gas or chemical liquids leaks it not only wastes resources, but also leads to more severe environmental pollution and possible human injury. Hence, for the safe operation and stable supply of the resource, periodic pipeline inspection and maintenance are urgently required.  
Pipeline inspection tasks mostly rely on two important techniques: leakage pre-localization and localization methods. The pre-localization method refers to a preliminary external detection system based on pressure or acoustic sensors, fiber optic networks, ground-penetrating radar, etc. This method can quickly estimate the approximate location of a leakage, however, the detection accuracy is limited. In order to obtain precise leakage information, localization methods based on mobile in-pipe inspection systems are required. These systems can further detect, classify, and localize the leakages in pipelines [2; 3; 4; 5].  
Recently, several in-pipe visual inspection approaches have been widely utilized for underground pipeline network inspection and maintenance. The entire visual inspection system is usually composed of a mobile device (e.g., robot), a camera, and a lighting device mounted around the camera. When the system moves along the pipe, it can transmit the detection image to the external monitor outside the pipe. During the inspection, the captured images can be identified, classified, and located by the operator. Although this method is time-consuming and labor-intensive, most of the internal corrosion, cracking, and deformation inside the pipe can be accurately diagnosed [6; 7; 8].  
The accurate acquisition and efficient transmission of pipeline internal image information with wireless approaches are extremely crucial for pipeline detection. However, there are still difficulties in realizing the above requirements due to the complexity of the underground pipeline environment and the attenuation effect of the transmitted signal. As the analysis in our former research, the visible light communication technique has exhibited some advantages in pipeline inspection. This article concentrates on the analysis of the in-pipe image transmission system based on visible light relay communication (VLRC) technology in order to realize reliable image transmission [9]. The transmission is carried out with two methods: analog image signal relay transmission (AISRT) and digital image frame relay transmission (DIFRT). Among them, discrete cosine transform (DCT) and inverse discrete cosine transform (IDCT) method are adopted for DIFRT. Moreover, the data frame and modulation approaches are specifically designed and implemented to increase the precision and efficiency of image transmission. The DIFRT can assist the system to reconstruct the in-pipe image information with smaller image information distortion and higher image transmission quality. It can basically satisfy VLRC-based image transmission for the pipe inspection [10].  
The rest of this article is organized as follows: Section 2 introduces the related works and Section 3 analyzes the image transmission channel and quality evaluation. Sections 4 and 5 explain the analog image signal and digital image frame relay transmission, respectively. Section 6 describes the experimental results and discussion, including the waveform test on analog/digital image signal, test of image transmission with different bit error rate (BER) or in the different mediums. In Section 7, we summarize the results and propose future works.