## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Full Image** & **Zone 1** & **Zone 2** & **Zone 3** & **Zone 4** \\ \hline \multicolumn{5}{c}{**8 \(\times\) 8 pixels bit size**} \\
3.124 & 3.558 & 3.439 & 2.955 & 2.909 \\ \hline \multicolumn{5}{c}{**4 \(\times\) 4 pixels bit size**} \\
2.884 & 3.195 & 3.012 & 2.790 & 2.685 \\ \hline \multicolumn{5}{c}{**3 \(\times\) 3 pixels bit size**} \\
2.650 & 2.668 & 2.586 & 2.747 & 2.671 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured Q-factor by zone and for Vertical stripes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pixel Size** & **Q-Factor** & **Errors** & **BER** \\ \hline \(8\times 8\) & 3.082 & 1 & \(1.0\times 10^{-3}\) \\ \(4\times 4\) & 2.473 & 25 & \(6.8\times 10^{-3}\) \\ \(3\times 3\) & 2.243 & 117 & \(12.5\times 10^{-3}\) \\ \(2\times 2\) & 0.574 & 18,097 & \(2.8\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bit error rate and Q-factor for Vertical stripes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pixel Size** & **Q-Factor** & **Errors** & **BER** \\ \hline \(8\times 8\) & 3.124 & 3 & \(9.0\times 10^{-4}\) \\ \(4\times 4\) & 2.884 & 8 & \(2.0\times 10^{-3}\) \\ \(3\times 3\) & 2.650 & 26 & \(4.0\times 10^{-3}\) \\ \(2\times 2\) & 1.699 & 2371 & \(4.5\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measured Q-factor by zone and for Horizontal stripes. there is wider tolerance to the anamorphic effects introduced by LCoS devices. Now, we do not display periodic diffractive elements and the figure of merit is not diffraction efficiency. In our present case, the binary intensity contrast between the ON and OFF levels in the LCoS is the important magnitude and this has a limited dependency with the orientation of the non-periodic stripes displayed. We divided the data image into various zones, as presented in Figure 9, to analyze the Q factor and the BER by zones. This kind of analysis helps us to detect problems in the uniformity of the illumination, or errors due to deformations in the image. In Tables 3 and 4, we show the Q-factor obtained in the different zones. In horizontal stripes (Table 3), Zone 1 corresponds to the left part on the image. We observe that. for every bit size. the worst Q-factor is obtained in Zone 3. Nevertheless, for vertical stripes (Table 4), we see that the Q-factor decreases with the number of the zone, that means from top to the bottom (Figure 9, left). This kind of analysis allows us to measure the illumination inhomogeneity: if the difference is too high, we would have to improve the experimental setup to avoid this source of error. We present results for the complete reconstruction process. When we introduce the photopolymer, new distortion elements appear. The manufacturing process can introduce ripples and deformations due to continuous exchange of water molecules between material and environment. In addition, the natural crystallization may affect the recording process [32]. In Figure 12, we show the image captured by the CCD during the reconstruction process. We show the reconstructed images for the \(8\times 8\) and \(4\times 4\) bit sizes to compare with the results obtained without material. The degradation effects increase the BER, and lower the Q-factor accordingly, as can be observed in Tables 5 and 6, for the analysis