# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 5 Experimental Evaluation
### Noise Analysis of the Actual D2C Link  
Let us assume that the original transmit image is image A, which is displayed on the screen. Now, the image is captured by a camera and subsequently distortion-corrected to obtain image B. In this case, the noise characteristics due to the optical wireless channel can simply be found by calculating the difference between the two images in the frequency domain. The experimental parameters for noise analysis are described in Table 3.  
The actual noise characteristics for the red channel observed in our experiment are shown in Figure 6. In particular, Figure 6a shows a three-dimensional view of the noise observed in a D2C link. For reference, we have included the input image to get a perspective on how pixels contribute to frequency-domain noise. We can observe that the noise is predominantly present in the upper part of the image. In other words, the most significant components of noise are present in the vicinity of row 0 as observed in Figure 6b. As already mentioned in Section 3.1, the upper part of an image consists of low-frequency components due to the characteristic of 1D-DCT operation. Therefore, we can observe that the actual noise components are present in the low-frequency bands contrary to our belief that the noise is mainly present at high frequencies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input Image & Lena color (\(256\times 256\) 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 3: Experimental parameters for noise analysis.  
Another interesting fact we can observe is that conventional Gaussian noise is considered to be white, which has a flat power spectral density (p.s.d.). However, the actual noise is not Gaussian and seems to be colored in nature having different power in different frequency bands. This is because the actual noise consists of corruptions of transmitting images due to many factors such as camera tilting, scaling, image rotation, illumination conditions, etc. In addition, if we get some error during the image extraction process at the camera, it will also contribute to noise. So, when we capture the transmitter display with a camera, all kinds of noise get added together to the captured image pixels. If we analyze this noise, we found that most noise sources are present in low-frequency bands and very few noise sources are present in mid- and high-frequency bands.  
Accordingly, from the noise analysis, we can find that 'which sub-band is best from the data-embedding perspective?' The data embedding area should be determined by appropriately considering the two factors of data decoding performance and image quality maintenance. From the data decoding perspective, it is optimal to embed the data in low-frequency components. In other words, data embedding from rows 0 to 10 are expected to have a good data decoding performance. However, if we look at Figure 6b, in this region, the noise components are very high. Therefore, it does not seem appropriate to embed the data at such high noise components. Therefore, we choose rows 5 to 15 as the proper data-embedding region in our experiment (shown in the red box). Although there are still some significant noise components in the given region, note that we are using the power allocation scheme to increase the power of the data pixels. Therefore, by properly adjusting the value of \(\alpha\) in Equation (7), we can confirm the performance with respect to the PSNR. Next, we proceed with our experiments with the given data embedding region and present experimental results according to various system parameters.