# 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  
To observe the performance of various no-reference image quality evaluation methods, this paper compares several representative methods on several public datasets. PLCC and SROCC are adopted to assess the image quality evaluation results. The experimental results in the table are taken from the reported references, and each comparison method group was tested on the same dataset. The comparison of different evaluation methods on the LIVE dataset and CSIQ dataset, TID2008 dataset and TID2013 dataset, BID dataset and CID2013 dataset are shown in Tables 5-7, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Distortion Type** & \begin{tabular}{c} **Number of** \\ **Reference Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Distorted Images** \\ \end{tabular} & \
\begin{tabular}{c} **Image Size** \\ **(Pixel)** \\ \end{tabular} &
\begin{tabular}{c} **Subjective** \\ **Scoring** \\ \end{tabular} & **Score Range** \\ \hline LIVE & Analog distortion & 29 & 779 & \(428\times 634\)–\(512\times 768\) & DMOS & \([1,100]\) \\ \hline CSIQ & Analog distortion & 30 & 866 & \(512\times 512\) & DMOS & \([0,9]\) \\ \hline TID2008 & Analog distortion & 25 & 1700 & \(512\times 384\) & MOS & \([0,9]\) \\ \hline TID2013 & Analog distortion & 25 & 3000 & \(512\times 384\) & MOS & \([0,9]\) \\ \hline BID & Natural distortion & - & 585 & \(1280\times 960\)–\(2272\times 1704\) & MOS & \([0,5]\) \\ \hline CID2013 & Natural distortion & - & 480 & \(1600\times 1200\) & MOS & \([0,100]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Specific information of the public datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**TID2008**} & \multicolumn{2}{c}{**TID2013**} \\ \cline{3-6}  & & **PLCC** & **SROCC** & **PLCC** & **SROCC** \\ \hline \multirow{4}{*}{Spatial domain-based} & MLV [46] & 0.811 & 0.812 & 0.883 & 0.879 \\ \cline{2-6}  & BIBLE [35] & 0.893 & 0.892 & 0.905 & 0.898 \\ \cline{2-6}  & MGV [46] & 0.937 & 0.942 & 0.914 & 0.921 \\ \cline{2-6}  & ARISM [47] & 0.854 & 0.868 & 0.898 & 0.902 \\ \cline{2-6}  & CPBD [49] & 0.824 & 0.841 & 0.855 & 0.852 \\ \hline \multirow{4}{*}{Spectral domain-based} & DCT-QM [60] & 0.819 & 0.837 & 0.852 & 0.854 \\ \cline{2-6}  & SC-QI [61] & 0.890 & 0.905 & 0.907 & 0.905 \\ \cline{2-6}  & FISH [62] & 0.816 & 0.786 & 0.911 & 0.912 \\ \cline{2-6}  & LPC-SI [65] & 0.846 & 0.843 & 0.892 & 0.889 \\ \cline{2-6}  & BISHARP [66] & 0.877 & 0.880 & 0.892 & 0.896 \\ \hline \multirow{4}{*}{Learning-based} & BIQI [72] & 0.794 & 0.799 & 0.825 & 0.815 \\ \cline{2-6}  & DB-CNN [77] & 0.873 & 0.859 & 0.865 & 0.816 \\ \cline{2-6}  & DeepBIQ [78] & **0.951** & **0.952** & 0.920 & 0.922 \\ \cline{2-6}  & SPARISH [79] & 0.889 & 0.887 & 0.900 & 0.893 \\ \cline{2-6}  & SR [81] & 0.895 & 0.911 & 0.899 & 0.892 \\ \cline{2-6}  & MSFF [87] & 0.926 & 0.917 & 0.917 & 0.922 \\ \hline \multirow{4}{*}{Combination} & S3 [88] & 0.851 & 0.842 & 0.879 & 0.861 \\ \cline{2-6}  & RFSV [91] & 0.915 & 0.924 & 0.924 & 0.932 \\ \cline{2-6}  & SVC [92] & 0.889 & 0.874 & 0.857 & 0.787 \\ \cline{2-6}  & SFA [93] & 0.916 & 0.907 & **0.954** & **0.948** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of different evaluation methods on TID2008 dataset and TID2013 dataset.