# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 5 Experimental Evaluation
### Performance According to Transmit Image Size
(**c**) \(512\times 512\) pixel size. To solve this problem, the next section proposes a way to improve image quality through repetitive data embedding. Image Quality Improvement through Repetitive Data Embedding  
Table 5 shows the experimental setting for two images of different sizes (\(128\times 128\) and \(256\times 256\) pixels) used in the experiment. In the experiment, \(150\) identical data bits are embedded in both images, and they are displayed on the same monitor to be affected by the same upsampling effect. As mentioned above, owing to the upsampling effect, the number of encoded data bits becomes four times higher in the case of image 2. Previously, we had mentioned that the effect of upsampling on a \(128\times 128\) pixel image is relatively larger than that on a \(256\times 256\) pixel image. Hence, the performance of a \(128\times 128\) pixel image is good. However, the image quality is distorted due to reduced PSNR. To solve this problem, we propose a method to obtain both performance and image quality by repeatably embedding four pixels data into the frequency domain of the \(256\times 256\) pixel image and obtaining the same upsampling effect as for the \(128\times 128\) pixel image. Figure 11 depicts the conventional data-embedding method and the proposed repetitive data-embedding method. Let the data we want to embed be a vector of the form [0, 1, 0, 0, 1, 1,...]. In the conventional method (left side), the data embedding descends from top to bottom in a straightforward manner. On the other hand, in the repetitive data-embedding method (right side), each dataset is repeated at four pixels into the image using the repetitive coding technique. In other words, four pixels represent one data bit of information, which means adding redundancy to data. Figure 10: Increased pixel appearance when the image was enlarged horizontally and vertically (\(K=2\)). Different colors indicate different pixel values. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Image 1** & **Image 2** \\ \hline Input Image & Lena color & Lena color \\ Input image size & \(128\times 128\) p & \(256\times 256\) p \\ No. of binary data bits & \(150\) & \(150\) \\ No. of encoded data bits & \(768\) & \(3072\) \\ Lower sub-band position & rows 3 to 9 & rows 5 to 17 \\ Distance & \(10\) cm & \(10\) cm \\ Camera angle & \(0^{\circ}\) & \(0^{\circ}\) \\ Image rotation & \(0^{\circ}\) & \(0^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental parameters for image quality improvement. Figure 12 depicts the BER comparison of two images according to PSNR. We have used repetitive coding in a \(256\times 256\) pixel image by embedding one data into four pixels. This is to obtain the same upsampling effect as in the case of a \(128\times 128\) pixel image. We can see that the \(256\times 256\) pixel image is showing performance closer to that of the \(128\times 128\) pixel image. This is much improved compared to Figure 8, where we have not used repetition coding. This similar performance is because the same upsampling ratio is obtained in both images utilizing the repetitive data-embedding process. Furthermore, we can also get better picture quality for the \(256\times 256\) pixel image than that of the \(128\times 128\) image, when the image is displayed on the screen. Figure 13 depicts the RMS EVM plots corresponding to Figure 12. The EVM plots also show similar performance in both image cases. This shows that by using repetitive coding, the performance of images having larger sizes can be made similar to images with smaller sizes. Figure 11: Comparison of the conventional and proposed repetitive data embedding method. Figure 12: Comparison of BER according to PSNR. (**a**) \(128\times 128\) pixel size. (**b**) \(256\times 256\) pixel size.