# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Results of Decolorization Methods
Pairwise comparisons for FSIM reveal that certain methods, like DL, perform significantly worse in preserving feature similarity compared to others such as Rec. 601 and Rec. 2020. For practical applications, methods like Rec. 601 and Rec. 2020 may be preferred for better FSIM performance, whereas DL may underperform in feature preservation. Metrics like GRR are crucial in applications requiring global contrast retention, while FSIM is more suited for perceptual quality evaluations. \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 & 0.3109 & 0.3212 & 0.3177 & 0.2991 & 0.3283 & **0.3331** & 0.2918 & 0.3146 \(\pm\) 0.015 \\ \hline Image 2 & 0.2917 & 0.2667 & 0.2753 & 0.3315 & **0.3343** & 0.3249 & 0.2243 & 0.2927 \(\pm\) 0.041 \\ \hline Image 3 & 0.3332 & 0.3360 & 0.3352 & 0.3181 & 0.3280 & 0.3346 & **0.3379** & 0.3319 \(\pm\) 0.007 \\ \hline Image 4 & 0.3355 & 0.3310 & 0.3350 & **0.3734** & 0.3303 & 0.3581 & 0.3079 & 0.3387 \(\pm\) 0.021 \\ \hline Image 5 & 0.3157 & 0.3221 & 0.3213 & 0.3127 & 0.3250 & **0.3411** & 0.3246 & 0.3232 \(\pm\) 0.009 \\ \hline Image 6 & 0.3214 & 0.3204 & 0.3222 & 0.3335 & 0.3200 & **0.3540** & 0.3326 & 0.3292 \(\pm\) 0.012 \\ \hline Image 7 & 0.2176 & 0.2259 & 0.2218 & **0.3298** & 0.2424 & 0.2786 & 0.2189 & 0.2479 \(\pm\) 0.042 \\ \hline Image 8 & 0.2267 & 0.2309 & 0.2268 & **0.3800** & 0.2546 & 0.3502 & 0.2010 & 0.2672 \(\pm\) 0.069 \\ \hline Image 9 & 0.0990 & 0.1177 & 0.1069 & 0.2129 & 0.1767 & **0.2872** & 0.0441 & 0.1492 \(\pm\) 0.082 \\ \hline Image 10 & 0.3083 & 0.2970 & 0.3051 & **0.4003** & 0.3406 & 0.3344 & 0.3405 & 0.3323 \(\pm\) 0.035 \\ \hline Image 11 & 0.3152 & 0.3199 & 0.3170 & 0.3301 & 0.3249 & **0.3347** & 0.3017 & 0.3205 \(\pm\) 0.011 \\ \hline Image 12 & 0.3302 & 0.3341 & 0.3333 & 0.3545 & 0.3256 & 0.2858 & **0.3588** & 0.3318 \(\pm\) 0.024 \\ \hline Image 13 & 0.0493 & 0.0320 & 0.0463 & 0.2002 & 0.1701 & **0.3217** & 0.0317 & 0.1216 \(\pm\) 0.112 \\ \hline Image 14 & 0.2864 & 0.2921 & 0.2896 & 0.3224 & 0.2986 & **0.3131** & 0.2950 & 0.2996 \(\pm\) 0.013 \\ \hline Image 15 & 0.3415 & 0.3461 & 0.3475 & 0.3228 & 0.3267 & **0.3631** & 0.3585 & 0.3437 \(\pm\) 0.015 \\ \hline Image 16 & 0.3269 & 0.3292 & 0.3292 & **0.3471** & 0.3322 & 0.3414 & 0.3257 & 0.3331 \(\pm\) 0.009 \\ \hline Image 17 & 0.3333 & 0.3398 & 0.3378 & 0.3471 & 0.3229 & 0.3421 & **0.3474** & 0.3386 \(\pm\) 0.009 \\ \hline Image 18 & 0.0634 & 0.1113 & 0.0907 & **0.3273** & 0.0615 & 0.2471 & 0.0587 & 0.1371 \(\pm\) 0.107 \\ \hline Image 19 & 0.3239 & 0.3362 & 0.3331 & 0.3146 & 0.3005 & **0.3535** & 0.3249 & 0.3267 \(\pm\) 0.017 \\ \hline Image 20 & 0.3202 & 0.3244 & 0.3247 & 0.3335 & 0.3074 & **0.3496** & 0.3371 & 0.3281 \(\pm\) 0.013 \\ \hline Image 21 & 0.3027 & 0.2979 & 0.3018 & **0.3749** & 0.3169 & 0.3090 & 0.2958 & 0.3141 \(\pm\) 0.028 \\ \hline Image 22 & 0.3351 & 0.3353 & 0.3355 & 0.3307 & 0.3334 & **0.3410** & 0.3197 & 0.3330 \(\pm\) 0.007 \\ \hline Image 23 & 0.3191 & 0.3226 & 0.3223 & 0.3245 & 0.3136 & 0.3147 & **0.3438** & 0.3229 \(\pm\) 0.010 \\ \hline Image 24 & 0.3183 & 0.3303 & 0.3234 & 0.2641 & 0.3177 & **0.3438** & 0.3267 & 0.3178 \(\pm\) 0.025 \\ \hline Image 25 & 0.3467 & 0.3504 & 0.3503 & 0.3640 & 0.3340 & 0.3530 & **0.3867** & 0.3550 \(\pm\) 0.017 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of GRR measurements. The best results are shown in bold. When analyzing the results from RMS, the GD colorization method stands out as the most effective (method mean = 579.729), followed closely by the DL colorization method (method mean = 519.834), as illustrated in Table 4. Among various image contents, the best performance is seen with Image 6 (image mean = 1073.39), which is characterized by very high contrast and well-defined edges.