# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Object Segmentation and Edge Detection on Decolorized Images  
Decolorization methods are used as a preparatory step for other image processing tasks, and their application can improve the results of object segmentation or edge detection within images. By removing color information and focusing on luminance and contrast, decolorization can reduce the complexity of data, making object segmentation and edge detection more efficient and accurate [4]. Moreover, because different colors can have similar grayscale intensities, effective decolorization methods often involve advanced techniques that retain meaningful distinctions between areas that might otherwise appear similar in grayscale. To examine how different decolorization methods affect object segmentation, we generated test images sized 256 \(\times\) 256 pixels containing six objects: two triangles, two squares, and two circles. These objects do not overlap and are positioned at defined locations with known coordinates, allowing for precise area calculations. The objects in each image are in fully saturated colors: red (255, 0, 0), blue (0, 0, 255), green (0, 255, 0), magenta (255, 0, 255), cyan (0, 255, 255), and yellow (255, 255, 0). They are all placed on a gray background with RGB component values equal to (128, 128, 128), on a white (255, 255, 255), and a black (0, 0, 0) background, respectively. For the following three images, the object colors are desaturated (255 in color definitions is replaced by 128) and placed on black, gray, and white backgrounds. For these six test images, shown in Figure 7, we applied previously mentioned decolorization methods, followed by object segmentation. The metrics for evaluating decolorization, applied to the six generated images, are presented in Tables 9-12. For RMS applied to the six generated images, the best results were achieved with the GD (method mean = 13.598) and DL (method mean = 12.619) decolorization methods, as shown in Table 9. The same applies to GRR (method mean for DL is equal to 0.420, and for GD 0.332), presented in Table 11. NRMS shows the best results for the CDF method (method mean = 0.087), as can be seen in Table 10, while FSIM favors the basic decolorization methods (method means for Rec. 709, 601, and 2020 are 0.985, 0.943, and 0.923, respectively), shown in Table 12. \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Time (s)** \\ \hline Basic methods & **0.00399** \\ \hline CDF method & 0.02602 \\ \hline GD method & 0.09695 \\ \hline DL method & 7400.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average execution time. The best results are shown in bold. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Test Image** & **CDF** & **DL** & **GD** & **LAB** & **Rec. 601** & **Rec. 709** & **Rec. 2020** & \begin{tabular}{c} **Image Mean** \\ \(\pm\) SD** \\ \end{tabular}  \\ \hline gray\_surface\_fully\_saturated & **0.070** & 0.100 & 0.090 & 0.100 & 0.080 & 0.080 & 0.080 & 0.080 \(\pm\) 0.011 \\ \hline white\_surface\_fully\_saturated & **0.070** & 0.100 & **0.070** & 0.080 & 0.080 & **0.070** & 0.080 & 0.079 \(\pm\) 0.011 \\ \hline black\_surface\_fully\_saturated & **0.120** & 0.150 & 0.150 & 0.140 & 0.140 & 0.130 & 0.140 & 0.139 \(\pm\) 0.011 \\ \hline gray\_surface\_not\_saturated & **0.070** & 0.100 & 0.080 & 0.100 & 0.080 & 0.080 & 0.080 \(\pm\) 0.011 \\ \hline white\_surface\_not\_saturated & **0.070** & 0.090 & 0.080 & 0.080 & 0.080 & **0.070** & 0.080 & 0.079 \(\pm\) 0.007 \\ \hline black\_surface\_not\_saturated & **0.120** & 0.140 & 0.160 & 0.140 & 0.130 & 0.130 & 0.130 \(\pm\) 0.013 \\ \hline Method mean \(\pm\) SD & \begin{tabular}{c} **0.087** \\ \(\pm\) **0.026** \\ \end{tabular}  & \begin{tabular}{c} 0.113 \\ \(\pm\) **0.025** \\ \end{tabular}  & \begin{tabular}{c} 0.105 \\ \(\pm\) 0.039 \\ \end{tabular}  & \begin{tabular}{c} 0.107 \\ \(\pm\) 0.027 \\ \end{tabular}  & \begin{tabular}{c} 0.098 \\ \(\pm\) 0.028 \\ \end{tabular}  & \ \begin{tabular}{c} 0.093 \\ \(\pm\) 0.029 \\ \end{tabular}  &
\begin{tabular}{c} 0.098 \\ \(\pm\) 0.