# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results  
### Results of Decolorization Methods  
Different methods of decolorization result in images that can be quite different from each other. However, sometimes it is almost impossible to notice the difference. Figure 4 shows an example of the original color Image 1 and Table 2 shows three decolorized images, obtained by applying the Rec. 2020, CDF, and DL methods to the original image. All three methods give different results. If we look at RMS, the best result is given by the Rec. 2020 method (565.764). If only NRMS is taken into account, the CDF method has the best score (0.1904). GRR considers the DL method as the best (0.3331). But, according to the results of FSIM, the Rec. 2020 method is the best (0.9608). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Rec. 2020** & **CDF** & **DL** \\ \hline RMS & 565.754 & 434.818 & 467.567 \\ \hline NRMS & 0.2031 & **0.1904** & 0.2033 \\ \hline GRR & 0.3177 & 0.3283 & **0.3331** \\ \hline FSIM & **0.9608** & 0.8968 & 0.6813 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Decolorized images and results for color Image 1. The best results are shown in bold. With the example of the original Image 5 shown in Figure 5, the results are clearer. Three decolorized images and the results of the measures are shown in Table 3. In this case, it is easier to determine which is better--different colors of billiard balls must be displayed in different shades of gray. The best distinction was achieved with the Rec. 2020 method because the shades of gray for different colors differ mostly with this method. The results of three metrics support this conclusion (RMS = 505.590, NRMS = 0.1475, and FSIM = 0.9276). The example of images decolorized using the Rec. 2020 method is shown in Figure 6. Rec. 2020 effectively produces different outcomes for different metrics, allowing the illustration of both satisfactory and unsatisfactory decolorization results. RMS is excluded due to non-normalized values. The \(y\)-axis shows the values of the metrics for the corresponding image, and the image indexes are on the \(x\)-axis. A certain degree of correlation is visible, where the growth of the value of one metric follows the growth of the value of the other, but with considerable deviations. As previously established, different metrics will often produce conflicting results. For example, using FSIM, Image 6 and Image 23 received the lowest ratings (0.0953 and 0.0395, respectively), indicating poorer decolorization quality. Figure 4: Original color Image 1. Figure 5: Original color Image 5. \begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Rec. 2020** & **CDF** & **DL** \\ \hline RMS & **505.590** & 491.316 & 500.824 \\ \hline NRMS & **0.1475** & 0.1381 & 0.1474 \\ \hline GRR & 0.3213 & **0.3250** & 0.3411 \\ \hline FSIM & **0.9276** & 0.8393 & 0.5651 \\ \hline \end{tabular}
\end{table} 
Table 3: Decolorized images and results for color Image 5. The best results are shown in bold. For GRR, the lowest results were achieved for Images 9, 13, and 18 (0.1069, 0.0463, and 0.0907, respectively), which belong to the category of generated images. For NRMS, Images 1, 2, and 10 have the highest results (0.2031, 0.1621, and 0.2206, respectively), which in this case indicates the poorest decolorization quality. These images have very saturated colors. The results depend on both the specific metric and the type and content of the images, so it is best to consider the results as averages for both the metrics and the images. Tables 4-7 show the results of individual measures on all decolorized images, method means, image means, and standard deviations (SD). For each image, the best result is shown in bold. ANOVA (analysis of variance) [33] was used to determine whether there are significant differences between the means of seven decolorization methods by analyzing the variability within and between methods. The Kolmogorov-Smirnov test for normality of a distribution shows that distributions for RMS and NRMS are likely normal, supporting the validity of ANOVA. The Levene test