# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Decolorization Methods
Conversely, the poorest results are associated with Image 18 (image mean = 85.9539), an artificially generated image that features low-saturation colors, making it difficult for the algorithms to accurately interpret the image. When considering the NRMS measure presented in Table 5, the CDF method emerges as the most successful (method mean = 0.0844), with Rec. 601 following closely behind (method mean = 0.0886). A closer examination of the results by image reveals that the best performance occurs with Image 22 (image mean = 0.0158), which contains text, Image 4 (image mean = 0.0218), which contains dominant white areas, and Image 8 (image mean = 0.0390), characterized by pastel colors. These types of images pose challenges for decolorization due to their lack of vivid color differentiation. For GRR, the best results \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Image** & **Rec. 601** & **Rec. 709** & **Rec. 2020** & **GD** & **CDF** & **DL** & **LAB** & **Image Mean \(\pm\) SD** \\ \hline Image 1 & **0.9673** & 0.9492 & 0.9608 & 0.8285 & 0.8968 & 0.6813 & 0.9059 & 0.8843 \(\pm\) 0.102 \\ \hline Image 2 & **0.9931** & 0.9605 & 0.9728 & 0.9229 & 0.9439 & 0.9398 & 0.8958 & 0.9470 \(\pm\) 0.032 \\ \hline Image 3 & **0.9941** & 0.9895 & 0.9920 & 0.9474 & 0.9846 & 0.9612 & 0.9770 & 0.9780 \(\pm\) 0.018 \\ \hline Image 4 & **0.9782** & 0.9740 & 0.9773 & 0.9060 & 0.9649 & 0.9193 & 0.9446 & 0.9520 \(\pm\) 0.029 \\ \hline Image 5 & **0.9566** & 0.9412 & 0.9276 & 0.7772 & 0.8393 & 0.5651 & 0.7929 & 0.8286 \(\pm\) 0.137 \\ \hline Image 6 & 0.0571 & 0.0389 & 0.0434 & **0.8798** & 0.0760 & 0.0140 & 0.0653 & 0.1678 \(\pm\) 0.315 \\ \hline Image 7 & **0.9477** & 0.9327 & 0.9375 & 0.7798 & 0.8798 & 0.8430 & 0.9265 & 0.8924 \(\pm\) 0.062 \\ \hline Image 8 & **0.9610** & 0.9369 & 0.9503 & 0.6948 & 0.8679 & 0.8108 & 0.9136 & 0.8765 \(\pm\) 0.096 \\ \hline Image 9 & **0.9175** & 0.7904 & 0.8272 & 0.6725 & 0.6777 & 0.6215 & 0.6313 & 0.7340 \(\pm\) 0.112 \\ \hline Image 10 & **0.9803** & 0.9269 & 0.9622 & 0.6227 & 0.8501 & 0.4058 & 0.8206 & 0.7955 \(\pm\) 0.210 \\ \hline Image 11 & 0.9154 & 0.8751 & **0.9397** & 0.8309 & 0.8891 & 0.6224 & 0.9274 & 0.8571 \(\pm\) 0.110 \\ \hline Image 12 & **0.9764** & 0.9655 & 0.9718 & 0.8981 & 0.9567 & 0.1837 & 0.9410 & 0.8419 \(\pm\) 0.291 \\ \hline Image 13 & **0.9114** & 0.8070 & 0.7978 & 0.5375 & 0.5474 & 0.0264 & 0.7119 & 0.6199 \(\pm\) 0.296 \\ \hline Image 14 & **0.9729** & 0.9566 & 0.9617 & 0.8985 & 0.9187 & 0.8973 & 0.9296 & 0.9336 \(\pm\) 0.031 \\ \hline Image 15 & **0.9716** & 0.9636 & 0.9662 & 0.8977 & 0.9421 & 0.9080 & 0.9474 & 0.9424 \(\pm\) 0.029 \\ \hline Image 16 & **0.9858** & 0.9711 & 0.9736 & 0.8055 & 0.9478 & 0.8077 & 0.9621 & 0.9219 \(\pm\) 0.080 \\ \hline Image 17 & 0.9537 & 0.9461 & 0.9497 & 0.9258 & **0.9573** & 0.4277 & 0.9344 & 0.8707 \(\pm\) 0.196 \\ \hline Image 18 & **0.8305** & 0.7167 & 0.7678 & 0.5018 & 0.6861 & 0.4948 & 0.7143 & 0.6731 \(\pm\) 0.128 \\ \hline Image 19 & **0.9876** & 0.9610 & 0.9705 & 0.8806 & 0.9110 & 0.9290 & 0.9450 & 0.9407 \(\pm\) 0.037 \\ \hline Image 20 & **0.9875** & 0.9676 & 0.9743 & 0.8691 & 0.8921 & 0.9279 & 0.9205 & 0.9341 \(\pm\) 0.044 \\ \hline Image 21 & **0.9721** & 0.9630 & 0.9695 & 0.7811 & 0.9532 & 0.9128 & 0.9558 & 0.9296 \(\pm\) 0.068 \\ \hline Image 22 & **0.9917** & 0.9879 & 0.9892 & 0.9518 & 0.9801 & 0.0122 & 0.9731 & 0.8409 \(\pm\) 0.366 \\ \hline Image 23 & 0.2499 & 0.2487 & 0.2491 & **0.9167** & 0.2302 & 0.3778 & 0.2281 & 0.3572 \(\pm\) 0.252 \\ \hline Image 24 & **0.9849** & 0.9679 & 0.9777 & 0.8134 & 0.9541 & 0.8938 & 0.9407 & 0.9332 \(\pm\) 0.061 \\ \hline Image 25 & 0.9292 & 0.9263 & 0.9278 & **0.9501** & 0.9188 & 0.9376 & 0.8851 & 0.9250 \(\pm\) 0.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of FSIM measurement. The best results are shown in bold.