# Review: A Survey on Objective Evaluation of Image Sharpness
## 3 Bibliometrics Analysis
### The Performance of the Representative Methods on Public Datasets
#### 3.2.1 Public Datasets and Evaluation Indicators
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Method Category** & **Method** & **Published Time** & **Characteristic** \\ \hline \multirow{8}{*}{Spatial domain-based} & Grayscale gradient-based & MLV [46] & 2014 & Calculate the maximum local change in the image \\ \cline{2-4}  & Grayscale gradient-based & BIBLE [35] & 2015 & Calculate gradients and Ichebichef moments of images \\ \cline{2-4}  & Edge detection-based & MGV [46] & 2018 & Calculate the maximum gradient and gradient change in the image \\ \cline{2-4}  & Other spatial domain-based & ARISM [47] & 2014 & Calculate the energy difference and contrast difference of the AR model coefficients for each pixel \\ \cline{2-4}  & Other spatial domain-based & CFBD [49] & 2011 & Calculate the cumulative probability of blur detection \\ \hline \multirow{8}{*}{Spectral domain-based} & Fourier transform-based & DCT-QM [60] & 2016 & Compute the weighted average L\({}^{2}\) norm in the DCT domain. \\ \cline{2-4}  & Fourier transform-based & SC-QI [61] & 2016 & \begin{tabular}{c} Structural contrast indices and DCT blocks are employed to characterize local image features and visual quality perception properties of various distortion types. \\ \end{tabular} \\ \cline{2-4}  & Fourier transform-based & FISH [62] & 2022 & Compute image derivatives and block-based DFT \\ \cline{2-4}  & Wavelet transform-based & LPC-SI [65] & 2013 & Calculate LPC intensity change \\ \cline{2-4}  & Wavelet transform-based & BISHARP [66] & 2018 & Calculate the root mean square of the image to obtain local contrast information \\ \hline \multirow{8}{*}{Learning-based} & Machine-based & BIQI [72] & 2010 & The NSS model was used to parameterize the sub-band coefficients and to predict the sharpness values. \\ \cline{2-4}  & Deep learning-based & DB-CNN [77] & 2020 & \begin{tabular}{c} Distorted images use two convolutional neural networks for feature extraction and bilinear pooling for quality prediction. \\ \end{tabular} \\ \cline{2-4}  & Deep learning-based & DeepBIQ [78] & 2018 & \begin{tabular}{c} Use features extracted from pretrained CNN as generic image descriptions. \\ \end{tabular} \\ \cline{2-4}  & Dictionary learning-based & SPARISH [99] & 2016 & \begin{tabular}{c} The image is represented as a block using a dictionary, and the energy of the block is calculated using the sparse coefficient, then normalized by the pooling layer to obtain a sharpness evaluation score. \\ \end{tabular} \\ \cline{2-4}  & Dictionary learning-based & SR [81] & 2016 & \begin{tabular}{c} Sparse representation (SR) is used to extract structural information, and a multi-scale spatial max-pooling scheme is introduced to represent image locality. \\ \end{tabular} \\ \cline{2-4}  & Other learning-based & MSFF [87] & 2019 & \begin{tabular}{c} Taking multiple features of the image as input and learn feature weights through end-to-end training to obtain evaluations \\ \hline \multirow{8}{*}{Combination} & Graycale + DCT & S3 [88] & 2012 & \begin{tabular}{c} Calculate the slope of the magnitude spectrum in the spectral domain and the spatial variation in the spatial domain of the image patch \\ \end{tabular} \\ \cline{2-4}  & DCT + SIFT & RRSV [91] & 2016 & \begin{tabular}{c} The blocks that compute the gradient maps are converted to DCT coefficients to obtain shape information, and scale-invariant feature transform (SIFT) is used to obtain saliency maps. \\ \end{tabular} \\ \cline{2-4}  & SVC + Gradient & SVC [92] & 2017 & \
\begin{tabular}{c} Image quality is characterized using the distribution of different structural changes and the extent of structural differences. \\ \end{tabular} \\ \cline{2-4}  & DCNN + SFA [93] & SFA [93] & 2019 &
\begin{tabular}{c} The pre-trained DCNN model is used to extract features, and after feature aggregation, the least squares regression is partially used for quality prediction. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Specific information of different methods for comparison.