# Towards Automatic Image Enhancement with Genetic Programming and Machine Learning
## 4 Experiments in Real Estate Image Marketing
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline Setup & _Components_ & _NIMA A._ & _NIMA T._ & _BRISQUE_ & _PHIL_ & _SSIM_ & _MSE_ \\ \hline \multirow{4}{*}{GP.A} & _Nothing_ & \(1.06\) & \(-0.24\) & \(0.62\) & \(0.53\) & \(0.54\) & \(9611\) \\  & _Both_ & \(0.44\) & \(-0.11\) & \(0.08\) & \(0.47\) & \(0.79\) & \(3912\) \\  & _Compare_ & \(0.72\) & \(-0.17\) & \(0.30\) & \(0.70\) & \(0.68\) & \(6221\) \\  & _Threshold_ & \(0.62\) & \(-0.15\) & \(0.18\) & \(0.35\) & \(0.70\) & \(5869\) \\ \hline \multirow{4}{*}{GP.B} & _Nothing_ & \(0.92\) & \(-0.31\) & \(0.50\) & \(0.50\) & \(0.65\) & \(6119\) \\  & _Both_ & \(0.40\) & \(-0.15\) & \(0.03\) & \(0.46\) & \(0.83\) & \(26\) \\  & _Compare_ & \(0.67\) & \(-0.23\) & \(0.21\) & \(0.69\) & \(0.74\) & \(4145\) \\  & _Threshold_ & \(0.54\) & \(-0.19\) & \(0.10\) & \(0.33\) & \(0.76\) & \(3806\) \\ \hline \multirow{4}{*}{GPC.C} & _Nothing_ & \(0.69\) & \(-0.27\) & \(-0.36\) & \(0.43\) & \(0.82\) & \(23\) \\  & _Both_ & \(0.31\) & \(-0.14\) & \(-0.32\) & \(0.41\) & \(0.90\) & \(1135\) \\  & _Compare_ & \(0.49\) & \(-0.20\) & \(-0.37\) & \(0.60\) & \(0.87\) & \(16\) \\  & _Threshold_ & \(0.41\) & \(-0.17\) & \(-0.34\) & \(0.28\) & \(0.86\) & \(16\) \\ \hline \end{tabular}
\end{table} 
Table 4: The values represent the average improvement of each pipeline configuration, using all of the GP solutions. All of the highlighted cells represent values in which the performance was better than _One-Click_. All of the _no-reference_ metrics values range from 1 to 10, where 1 means the lowest quality, and 10 highest quality. The improvement is calculated by subtracting the original score from the the resulting one. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{2}{c}{**Fit.**} & _NIMA A._ & _NIMA T._ & _BRISQUE_ & _PHIL_ & _SSIM_ & _MSE_ \\ \hline \(\mu\) & One-Click & \(0.16\) & \(-0.02\) & \(-1.19\) & \(0.39\) & \(0.80\) & \(5250\) \\ \hline \(\sigma\) & One-Click & \(0.25\) & \(0.23\) & \(0.69\) & \(0.69\) & \(0.07\) & \(4175\) \\ \hline \(\mu\) & Classical List (CL) & \(0.43\) & \(-0.06\) & \(-0.08\) & \(0.25\) & \(0.66\) & \(3805\) \\ \hline \(\sigma\) & Classical List (CL) & \(0.31\) & \(0.26\) & \(1.86\) & \(0.64\) & \(0.09\) & \(2407\) \\ \hline \(\mu\) & \(GAN\)_A_ & \(0.08\) & \(-0.04\) & \(-0.22\) & \(0.15\) & \(0.92\) & \(677\) \\ \hline \(\sigma\) & \(GAN\)_A_ & \(0.16\) & \(0.16\) & \(0.67\) & \(0.30\) & \(0.05\) & \(317\) \\ \hline \(\mu\) & \(GAN\)_B_ & \(-0.14\) & \(-0.14\) & \(-0.37\) & \(-0.05\) & \(0.90\) & \(941\) \\ \hline \(\sigma\) & \(GAN\)_B_ & \(0.17\) & \(0.18\) & \(0.92\) & \(0.66\) & \(0.05\) & \(538\) \\ \hline \(\mu\) & GAN + CL & \(0.51\) & \(-0.10\) & \(0.13\) & \(0.38\) & \(0.61\) & \(4577\) \\ \hline \(\sigma\) & GAN + CL & \(0.36\) & \(0.27\) & \(1.52\) & \(0.69\) & \(0.08\) & \(2368\) \\ \hline \(\mu\) & GP.A & \(0.97\) & \(-0.13\) & \(0.63\) & \(0.44\) & \(0.58\) & \(8703\) \\ \hline \(\sigma\) & GP.A & \(0.40\) & \(0.35\) & \(1.43\) & \(0.85\) & \(0.08\) & \(4120\) \\ \hline \(\mu\) & GP.B & \(0.88\) & \(-0.21\) & \(0.20\) & \(0.51\) & \(0.69\) & \(5574\) \\ \hline \(\sigma\) & GP.B & \(0.41\) & \(0.30\) & \(1.51\) & \(0.79\) & \(0.11\) & \(3738\) \\ \hline \(\mu\) & GPC & \(0.58\) & \(-0.17\) & \(-0.53\) & \(0.37\) & \(0.88\) & \(1349\) \\ \hline \(\sigma\) & GPC & \(0.30\) & \(0.24\) & \(1.03\) & \(0.69\) & \(0.04\) & \(591\) \\ \hline \end{tabular}
\end{table} 
Table 3: Average (\(\mu\)) and standard deviation (\(\sigma\)) of the improvement and similarity using all of the setups and configurations on the test dataset. Highlighted are the cells that presented the best average score in each metric. All of the _no-reference_ metrics values range from 1 to 10, where 1 means the lowest quality, and 10 highest quality. The improvement is calculated by subtracting the original score from the the resulting one. SSIM goes from 0 to 1; higher is better. In MSE, lower is better.