# Review: A Survey on Objective Evaluation of Image Sharpness
## 3 Bibliometrics Analysis
### The Performance of the Representative Methods on Public Datasets
#### 3.2.2 Performance Analysis of Representative Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LIVE**} & \multicolumn{2}{c}{**CSIQ**} \\ \cline{3-6}  & & **PLCC** & **SROCC** & **PLCC** & **SROCC** \\ \hline \multirow{4}{*}{Spatial domain-based} & MLV [46] & 0.938 & 0.937 & 0.894 & 0.851 \\ \cline{2-6}  & BIBLE [35] & 0.962 & 0.961 & 0.940 & 0.913 \\ \cline{2-6}  & MGV [46] & 0.960 & 0.963 & 0.907 & 0.950 \\ \cline{2-6}  & ARISM [47] & 0.959 & 0.956 & 0.948 & 0.931 \\ \cline{2-6}  & CPBD [49] & 0.895 & 0.918 & 0.882 & 0.885 \\ \hline \multirow{4}{*}{Spectral domain-based} & DCT-QM [60] & 0.925 & 0.938 & 0.872 & 0.926 \\ \cline{2-6}  & SC-QI [61] & 0.937 & 0.948 & 0.927 & 0.943 \\ \cline{2-6}  & FISH [62] & 0.904 & 0.841 & 0.923 & 0.894 \\ \cline{2-6}  & LPC-SI [65] & 0.922 & 0.950 & 0.906 & 0.893 \\ \cline{2-6}  & BISHARP [66] & 0.952 & 0.960 & 0.942 & 0.927 \\ \hline \multirow{4}{*}{Learning-based} & BIQI [72] & 0.920 & 0.914 & 0.846 & 0.773 \\ \cline{2-6}  & DB-CNN [77] & 0.970 & 0.968 & 0.959 & 0.946 \\ \cline{2-6}  & DeepBIQ [78] & 0.912 & 0.893 & **0.975** & **0.967** \\ \cline{2-6}  & SPARISH [79] & 0.956 & 0.959 & 0.938 & 0.914 \\ \cline{2-6}  & SR [81] & 0.961 & 0.955 & 0.950 & 0.921 \\ \cline{2-6}  & MSFF [87] & 0.949 & 0.950 & - & - \\ \hline \multirow{4}{*}{Combination} & S3 [88] & 0.943 & 0.944 & 0.893 & 0.911 \\ \cline{2-6}  & RFSV [91] & **0.974** & **0.971** & 0.942 & 0.920 \\ \cline{1-1} \cline{2-6}  & SVC [92] & 0.949 & 0.941 & 0.952 & 0.954 \\ \cline{1-1} \cline{2-6}  & SFA [93] & 0.942 & 0.953 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of different evaluation methods on LIVE dataset and CSIQ dataset. As can be seen from Table 5, RFSV and DeepBIQ have the highest score and best performance on the LIVE and CSIQ analog distortion databases, respectively. Table 6 shows that DeepBIQ and SFA have the highest PLCC and SROCC values and the best performance on TID2008 and TID2013 analog distortion databases, respectively. From Table 7, the best performers on BID and CID2013 natural distortion datasets are SFA and BIQI, respectively. The spatial domain-based methods are generally based on basic image processing, with the obtained results intuitive. However, although the low-level features extracted with spatial domain-based methods are rich in local details, global semantic information is missing, causing the evaluation accuracy to be unsatisfactory. The performance of the spectral domain-based methods tends to fluctuate significantly depending on different datasets, to be specific, well-performed on analog distortion datasets while less effect on natural distortion datasets. It is because the spectral domain-based methods are excellent on high-frequency detail information analysis. The learning-based methods outperform other methods on the analog distortion datasets, but the performance on the four natural distortion datasets varies greatly. Combination methods can flexibly combine the above methods in a variety of ways to meet the characteristics of different images, thus could achieve good scores on their target dataset. However, currently, no one method can guarantee the optimal effect on all datasets. Figures 9 and 10 are the PLCC and SROCC values of different groups of methods on the analog distortion datasets, respectively, and Figure 11 shows the PLCC and SROCC values of different groups of methods on the natural distortion datasets. The data in Figures 9-11 are all taken from the average of the comparison results in Tables 5-7. It can be concluded that most of the methods based on spatial-domain and c