# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 3 Experimental Results and Analysis  
### Distance Factor Comparison Experiment  
In order to fully compare the performances of the three distance factors and, thus, decide which distance factor should be introduced into the sharpness evaluation index, two experiments were conducted for this section based on whether the image contents were the same. The first experiment was set up as follows. Firstly, 11 \"cameraman\" images with the same image contents but gradually increasing blur were selected, as shown in Figure 16. Then these images were evaluated by the sharpness evaluation model after introducing three different distance factors, respectively. Finally, the obtained evaluation values were plotted as scatter plot and least squares fitted with polynomial functions, as shown in Figure 17.  
Figure 15: Flow chart of sharpness evaluation model. In Figure 17, the blue, green, and red lines are the fitted lines of the scatter points of the image sharpness evaluation values after the introduction of the three distance factors of type 1, type 2, and type 3, respectively. In Table 1, CC is the Pearson linear correlation coefficient and ROCC is the Spearman rank-order correlation coefficient. A higher CC value indicates that the evaluation method is more effective; a larger ROCC values indicates that the evaluation method is more monotonic. From the data in Table 1, it is clear that the evaluation method after introducing the type 3 distance factor performed better in terms of both accuracy and predicted monotonicity. Therefore, when the contents of measured images are the same, the type 3 distance factor performs better. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Distance Factor** & **CC** & **ROCC** \\ \hline \multirow{3}{*}{I} & Type 1 & 0.8869 & 0.9545 \\ \cline{2-4}  & Type 2 & 0.9369 & 0.9727 \\ \cline{2-4}  & Type3 & **0.9568** & **0.9909** \\ \hline \multirow{3}{*}{II} & Type 1 & 0.9509 & 0.9818 \\ \cline{2-4}  & Type 2 & **0.9514** & **0.9909** \\ \cline{2-4}  & Type3 & 0.9502 & **0.9909** \\ \hline \hline \end{tabular}  
* The highest performances are shown in boldface.  
\end{table} 
Table 1: Performance of three distance factors. Figure 16: “Cameraman” with increasing blur. Figure 17: The relationship between the evaluation value and the degree of image blurring when the image contents are the same. The second experiment was different from the first experiment. Its selected images were all from the Gaussian blurred images of the LIVE database. These images had different blurring degrees and were not correlated with each other, as shown in Figure 18.  
Using the same processing method as the first experiment, Figure 19 was obtained. It should be noted that the abscissa in Figure 19 was not the order of the measured images but the subjective evaluation DMOS values of the corresponding measured images. The reason is that the measured images in the first experiment were generated by artificially applying Gaussian blur evenly. Therefore, it is reasonable to perform a linear fit to the scatter points of the evaluation values. However, the measured images selected in the second experiment were Gaussian blurred images in the LIVE database; their blurring degrees did not increase uniformly. At this time, it was obviously wrong to linearly fit the scatter points. Therefore, it was better to select the subjective evaluation DMOS values in the LIVE database as the abscissa and then to perform a linear fit to the scatter points. Observing the data of the second experiment in Table 1, it is easy to find that the CC values of the evaluation methods corresponding to the three distance factors were almost the same, while the ROCC value of the evaluation method corresponding to the type 1 distance factor was smaller compared to those of the evaluation methods corresponding to the type 2 and type 3 distance factors.