# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Decolorization Methods
\begin{table}
\begin{tabular}{l 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 & 519.617 & 582.028 & 565.764 & **621.117** & 434.818 & 467.567 & 436.141 & 518.15 \(\pm\) 74.364 \\ \hline Image 2 & 797.456 & 728.226 & 752.534 & **945.223** & 914.261 & 911.029 & 587.567 & 805.185 \(\pm\) 128.338 \\ \hline Image 3 & 877.524 & **886.037** & 881.577 & 865.701 & 872.535 & 848.373 & 776.973 & 858.389 \(\pm\) 37.971 \\ \hline Image 4 & 489.976 & 479.831 & 488.114 & **576.893** & 481.524 & 482.468 & 296.938 & 470.821 \(\pm\) 84.147 \\ \hline Image 5 & 495.731 & 509.275 & 505.59 & **552.341** & 491.316 & 500.824 & 403.181 & 494.037 \(\pm\) 44.863 \\ \hline Image 6 & 1095.02 & **1105.4** & 1103.26 & 1075.34 & 1076.51 & 1032.35 & 1025.88 & 1073.39 \(\pm\) 32.506 \\ \hline Image 7 & 336.302 & 340.467 & 338.938 & **512.95** & 343.215 & 340.433 & 323.476 & 362.254 \(\pm\) 66.763 \\ \hline Image 8 & 137.885 & 139.921 & 140.145 & **259.07** & 148.164 & 199.189 & 64.023 & 155.485 \(\pm\) 60.341 \\ \hline Image 9 & 132.584 & 181.982 & 152.3 & **378.531** & 270.695 & 356.574 & 18.366 & 213.005 \(\pm\) 129.318 \\ \hline Image 10 & 482.349 & 503.207 & 503.6 & **566.223** & 452.591 & 506.406 & 398.211 & 487.512 \(\pm\) 52.087 \\ \hline Image 11 & 498.804 & 515.913 & 509.512 & **529.915** & 495.215 & 432.296 & 350.695 & 476.05 \(\pm\) 63.408 \\ \hline Image 12 & 817.558 & **844.614** & 832.494 & 753.333 & 786.878 & 734.456 & 728.762 & 785.442 \(\pm\) 47.595 \\ \hline Image 13 & 74.327 & 71.24 & 74.852 & **260.582** & 164.733 & 229.816 & 63.307 & 134.122 \(\pm\) 83.809 \\ \hline Image 14 & 483.349 & 495.262 & 492.679 & **570.453** & 474.287 & 493.134 & 443.31 & 493.211 \(\pm\) 38.549 \\ \hline Image 15 & 392.325 & **407.119** & 405.733 & 406.332 & 360.4 & 385.568 & 333.564 & 384.434 \(\pm\) 27.919 \\ \hline Image 16 & 409.648 & 410.161 & 412.117 & **470.219** & 420.589 & 437.848 & 314.978 & 410.794 \(\pm\) 47.510 \\ \hline Image 17 & 600.573 & **604.664** & 603.303 & 598.067 & 594.29 & 534.312 & 519.322 & 579.219 \(\pm\) 36.217 \\ \hline Image 18 & 38.252 & 79.895 & 56.732 & **261.511** & 40.468 & 89.711 & 35.108 & 85.9539 \(\pm\) 80.265 \\ \hline Image 19 & 650.227 & **672.287** & 669.238 & 632.843 & 609.694 & 631.447 & 560.787 & 632.36 \(\pm\) 38.538 \\ \hline Image 20 & 742.195 & **762.282** & 755.194 & 722.566 & 721.103 & 728.297 & 656.862 & 726.928 \(\pm\) 34.752 \\ \hline Image 21 & 219.6 & 210.834 & 219.02 & **340.57** & 217.581 & 240.033 & 167.168 & 230.687 \(\pm\) 53.262 \\ \hline Image 22 & 567.516 & 574.505 & 571.564 & **587.513** & 563.174 & 582.219 & 411.133 & 551.089 \(\pm\) 62.272 \\ \hline Image 23 & 825.479 & 822.25 & 822.369 & 816.709 & **837.261** & 741.312 & 702.836 & 795.459 \(\pm\) 51.725 \\ \hline Image 24 & 605.324 & 598.474 & 605.676 & **773.089** & 595.156 & 675.119 & 529.845 & 626.098 \(\pm\) 77.291 \\ \hline Image 25 & 406.416 & 409.348 & 408.403 & **416.133** & 401.173 & 415.066 & 374.138 & 404.382 \(\pm\) 14.272 \\ \hline Method & 507.841 & 517.409 & 514.828 & **579.729** & 510.705 & 519.834 & 420.903 & \\ mean \(\pm\) SD & \(\pm\) 265.585 & \(\pm\) 261.401 & \(\pm\) 263.405 & \(\pm\) 213.154 & \(\pm\) 255.460 & \(\pm\) 229.194 & \(\pm\) 247.874 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of RMS measure. The best results are shown in bold. for homogeneity of variances shows no significant results for RMS and NRMS metrics (\(p>0.95\) for RMS and NRMS). This indicates that the assumption of homoscedasticity is met. ANOVA is appropriate for these metrics as the data shows no significant deviations from normality. The ANOVA test for RMS shows no significant difference between the decolorization methods, with a \(p\)-value of 0.515. Similarly, the ANOVA test for NRMS reports a \(p\)-value of 0.514, indicating no statistically significant difference between methods. It implies that all methods provide comparable performance.