# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 5 Experimental Evaluation
### Performance According to Different Input Images  
Until now, we have performed all the experiments with the standard Lena image as input on the transmitter screen. However, an actual video for DFC could have multiple image frames having different image features. Hence, in this section, we experiment using different input images. We use four standard images of Baboon, Fruits, Barbara, and Parrots as shown in Figures 20 and 21, respectively. All the images are of the same size as [PERSON]'s image, i.e., \(256\times 256\) pixels. In the case of the Baboon image, we have evenly distributed RGB components and in the case of the Fruits image, the red and blue channels have a smaller distribution than the green channel. Therefore, the following experiments allow us to visualize the performance of our system for images having different RGB color distributions. Table 7 indicates the rest of the experimental parameters.  
Figure 19: BER performance according to display rotation.  
Figure 20: Standard images. (**a**) Baboon. (**b**) Fruits.  
Figure 22 shows the BER performance of the proposed scheme according to PSNR for the Baboon and Fruits test images. As the Baboon image has evenly distributed RGB components, it results in similar errors for all three channels after the PSNR of 40 dB. In the case of the fruits image, the red and blue channels had a smaller distribution than the green channel. Hence, the error rate in the red and blue channels is worse than that in the green channel.  
From Figure 21, we can observe that the Barbara image has numerous lines, which tests the properties of resizing, denoising, and encoding/decoding algorithms of the proposed system. Moreover, it has more distribution of red and green components than blue components. On the other hand, the parrots image shows an even distribution of red and green colors and a low distribution of blue components. In Figure 23a, we can observe an error in the blue channel only whereas green and red channels are error-free. This is because the blue channel is comparatively less distributed. On the other hand, in Figure 23b, the red and green channels show very less error compared to the blue channel. This is because the red and green components are evenly distributed in the image and the blue component is very less.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input Image & Baboon, Fruits, Barbara, and Parrots color \\ Input image size & \(256\times 256\) p \\ No. of binary data bits & 500 \\ No. of encoded data bits & 2518 \\ Lower sub-band position & rows 5 to 15 \\ Distance & 28 cm \\ Camera angle & \(0^{\circ}\) \\ Image rotation & \(0^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental parameters for different input images.  
Figure 21: Standard images. (**a**) Barbara. (**b**) Parrots.  
Figure 22: BER performance according to PSNR for standard images (**a**) Baboon. (**b**) Fruits.  
Overall, it can be observed that the DFC performance also depends on the RGB color distribution in the spatial domain or the RGB frequency component distribution in the frequency domain. In general, we can observe that for different input images, the error performances are little bit different because of different RGB values. However, the tendency is similar. In addition, we can also notice that the green channel outperforms the blue and red channels in almost all the experiments whereas the blue channel performed the worst.