# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 6 Conclusions  
DFC is a novel D2C communication paradigm that provides dual-mode, full-frame communication, which enables concurrent delivery of primary video content to users and additional information to devices over D2C visual links without impairing user-viewing experience. This paper presented a DFC system in a practical indoor laboratory environment. First, DCT was used for converting the spatial-domain image into its spectral domain. In addition, we proposed a power allocation scheme and adopted turbo channel coding to improve the system's performance. To further improve the BER and get reliable communication, a deep learning scheme (YOLOv4) for object detection was used to extract the display area from the captured image. After extracting the display area, the distortion is corrected using the image processing algorithms. Furthermore, repetitive data encoding is used to reduce the impact of oversampling and downsampling on communication performance.  
From the experiments, we obtained the motivating results that the DFC is possible in a real-world scenario. In particular, an error-free communication is obtained for a display-camera distance of approximately 1 m in the red and green channels. Moreover, we realized that the smaller the image size, the lower the communication error rate. Furthermore, we have shown that even if the angle of the display or camera deviates by about \(\pm 40^{\circ}\), the error rate for all the channels was close to zero. On the other hand, when the display itself was rotated by \(\pm 60^{\circ}\), we show that errors are observed as the four-point distortion correction was not performed accurately. Finally, by proceeding experiments with different input images, we found that the performance differs slightly depending on the RGB frequency coefficients' distribution of the images, but shows a similar tendency. In addition, in all the experiments, the green channel performed well regardless of input images and experimental parameters.  
DFC, unlike conventional approaches, embeds the data in the frequency domain of an image rather than directly in the spatial domain. With this embedding, the DFC can work even with very small frame rates (e.g., 1 frame/s) as the images are not distorted in the spatial domain. However, the work on DFC is still in its infancy and there are several challenges to be solved, including channel estimation, frame synchronization, and boosting the data rate. As a future work, the authors are working on the video DFC with practical design and performance analysis. In particular, we embed different data in each sequential  
Figure 23: BER Performance according to PSNR for standard images. (**a**) Barbara. (**b**) Parrots.  
frame of a video and propose a system that works with videos. Finally, we make this DFC encoding and decoding process into an end-to-end network using deep learning.  
Conceptualization, S.-Y.J.; methodology, Y.-J.K. and S.-Y.J.; software, Y.-J.K.; validation, Y.-J.K., P.S. and S.-Y.J.; formal analysis, Y.-J.K.; investigation, Y.-J.K.; resources, S.-Y.J.; data curation, Y.-J.K.; writing--original draft preparation, Y.-J.K.; writing--review and editing, P.S. and S.-Y.J.; visualization, Y.-J.K., P.S. and S.-Y.J.; supervision, P.S. and S.-Y.J.; project administration, S.-Y.J.; funding acquisition, Y.-J.K. and P.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Gyeongbuk Regional Wind Energy Cluster Human Resources Development Project (20214000000010) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2022R1G1A1004799).  
Not applicable.  
The authors declare no conflict of interest.