# Finding the Least Motion-Blurred Image by Reusing Early Features of Object Detection Network
## 3 Results  
The results of the experimental comparison of the image quality evaluation methods are summarized in Table 2. Images are of the mannequin head. Results are listed for separate image pair types, for harder image pairs combined (image pairs that are the most similar according to the motion blur level), and for overall results. Harder image pairs are highlighted with a dark background. Our proposed method has ten variants (#1-#10)--they differ by the feature map sequence number from which the features were sampled for motion blur evaluation. The \"FMA\" part in the title of the method designates the source map of the features (see Table 1). Other methods were sharpness (#11), which was used in the previous research [86] to pick the sharpest image from the set. The three remaining methods (#12-#14) are well-known in reference-free (blind) image quality evaluation tasks. The top performing method quite consistently was our proposed LightGBMRanker-based using features from the  
Figure 4: Construction of the training (**a**) and test (**b**) datasets by augmenting original image. Here II—original image; I2, I3, I4, 15—augmented images; AT—affine transformation of the image; MB1–MB4—motion blurring from the lowest (#1) to the highest (#4) level. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Method**}} & \multicolumn{10}{c}{**Results of Selecting a Less Motion-Blurred Image from a Pair [\% Correct Selections]**} \\  & **I1-I2** & **I1-I3** & **I1-I4** & **I1-I5** & **I2-I3** & **I2-I4** & **I2-I5** & **I3-I4** & **I3-I5** & **I4-I5** & **Avg. of Hard** & **Overall** \\ \hline
1. FM1-LightGBM & 58.7 & 75.1 & 86.2 & 92.2 & 70.4 & 83.6 & 90.9 & 71.8 & 83.4 & 70.9 & 67.9 & 78.3 \\
2. FM2-LightGBM & 63.7 & 88.4 & 96.4 & 98.9 & 84.3 & 96.9 & 99.1 & 87.9 & 96.9 & 87.5 & 80.8 & 90.0 \\
3. FM3-LightGBM & 66.7 & 97.0 & 99.7 & 99.9 & 97.3 & 99.8 & **100** & 98.3 & 99.7 & 96.3 & 89.7 & 95.5 \\
4. FM4-LightGBM & 79.2 & 97.8 & 99.8 & **100** & 97.5 & 99.8 & 99.9 & 97.8 & 99.2 & 94.8 & 92.3 & 96.6 \\
5. FM5-LightGBM & 95.1 & 99.6 & 99.9 & **100** & 99.3 & 99.9 & **100** & **99.8** & 100 & 99.7 & 98.5 & 99.3 \\
6. FM6-LightGBM & 96.0 & 99.8 & **100** & **100** & 99.9 & **100** & **100** & **100** & 100 & 99.0 & 99.6 \\
7. FM7-LightGBM & 98.1 & **100** & **100** & 99.9 & **100** & **100** & **100** & **100** & 100 & 99.5 & 99.8 \\
8. FM8-LightGBM & **99.2** & **100** & **100** & **100** & 99.9 & **100** & **100** & **100** & **100** & **99.8** & **99.9** \\
9. FM9-LightGBM & 98.9 & **100** & **100** & **100** & **100** & **100** & **100** & **100** & **100** & 99.7 & **99.9** \\
10. FM10-LightGBM & 94.4 & 99.7 & **100** & **100** & 99.6 & 99.9 & **100** & 99.9 & **100** & 99.8 & 98.4 & 99.3 \\
11. Sharpness & 82.5 & 88.0 & 89.6 & 90.0 & 90.8 & 91.4 & 91.6 & 90.7 & 90.9 & 98.9 & 88.0 & 89.3 \\
12. BRISQUE & 81.5 & 95.5 & 98.1 & 99.0 & 95.2 & 98.1 & 99.0 & 97.4 & 98.6 & 97.1 & 92.8 & 95.9 \\
13. NIQUE & 65.0 & 98.3 & 99.9 & 99.9 & 99.8 & **100** & **100** & **98.7** & 99.2 & 86.9 & 87.6 & 94.8 \\
14. PIQUE & 67.9 & 87.7 & 95.8 & 98.3 & 81.3 & 95.2 & 98.2 & 86.4 & 95.5 & 86.2 & 80.4 & 89.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Method comparison results of selecting a less motion-blurred image from a pair of images. Images are of real faces. Values in the table are the percentages of selections that were correct. Images in the pairs are the following: I1—original image; I2—AT+MB1 (original image altered by affine transformation + motion blur #1); I3—AT\(\times\)2+MB2 (original image altered by the previous affine transformation twice + motion blur #2); I4—AT\(\times\)3+MB3; I5—AT\(\times\)4+MB4. The best performance in each comparison group (column) is highlighted in red. The dark background of the cells highlights comparison results of harder image pairs, i.e., image pairs that are the most similar according to the motion blur level.