# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 4 Experimental Results
(**right**) Objective results by our proposed algorithm on the same image. Figure 2: An example of a source reference image and eight retargeted images with different models. algorithms show smaller scores correspondence with the subjective assessment, although SIFT Flow and EH achieves higher scores for images classified as containing apparent texture and geometric structures. RR IQA for non-retargeted images algorithms show some better than FR IQA for retargeted images. The near-zero correlation for nearly all image types suggests they cannot predict well the subjective assessment. Their unsatisfying performance has to do with both the image features they use for measuring the distance, and with the way they construct correspondence between the images. The performance comparison with different IQA algorithms is given in Table 2. It is easy to find that our method is better than the given FR IQA for retargeted images and the given RR IQA for non-retargeted images in Table 2.  
PLCC is linear correlation metric, SRCC and KRCC are nonparametric rank correlation metric, and the agreement range is \([-1,\,1]\). In this way, higher correlation coefficient predicates higher sequences agreement. RMS is an error metric, so lower error value represents higher agreement. By comparing the image patches around SIFT feature points between reference and retargeted images, EMD provided a more robust metric of their similarity. As a result, our algorithm was able to obtain high quality prediction accuracy with the limited amount of RR features, and our algorithm could achieve good results in experiment. \begin{table}
\begin{tabular}{l l l l l l l l} \hline  & **Algorithm** & **Lines/Edges** & **Faces/People** & **Texture** & **Foreground Objects** & **Geometric Structures** & **Symmetry** \\ \hline \multirow{4}{*}{FR IQA for retargeted images} & EL & 0.043 & \(-\)0.076 & \(-\)0.060 & \(-\)0.079 & 0.103 & 0.298 \\  & CL & \(-\)0.023 & \(-\)0.181 & \(-\)0.071 & \(-\)0.183 & \(-\)0.009 & 0.214 \\  & BDW & 0.031 & 0.048 & \(-\)0.048 & 0.060 & 0.004 & 0.119 \\  & BDS & 0.040 & 0.190 & 0.060 & 0.167 & \(-\)0.004 & \(-\)0.012 \\  & SIFT Flow & 0.097 & 0.252 & **0.119** & 0.218 & 0.085 & 0.071 \\ \hline RR IQA & DCT & 0.124 & -0.002 & 0.051 & 0.201 & 0.035 & 0.103 \\  & VIF & 0.210 & 0.233 & 0.078 & 0.230 & 0.101 & \(-\)0.004 \\  & DNT & 0.142 & 0.248 & \(-\)0.025 & 0.185 & 0.046 & 0.151 \\  & RRED & \(-\)0.031 & 0.341 & 0.075 & 0.284 & \(-\)0.003 & 0.293 \\  & RRSSIM & 0.179 & 0.328 & 0.020 & 0.257 & **0.237** & 0.226 \\ \hline RR IQA for retargeted images & LEMD & 0.236 & 0.432 & 0.068 & 0.379 & 0.026 & 0.332 \\  & GEMD & 0.125 & 0.452 & 0.025 & 0.420 & 0.012 & 0.150 \\  & QS & **0.262** & **0.461** & 0.073 & **0.438** & 0.034 & **0.357** \\ \hline \end{tabular}
\end{table} 
Table 1: Correlation scores of subjective and objective measures for the KRCC according to image attributes. Highest score in each column appears in bold. \begin{table}
\begin{tabular}{l l l l l l} \hline  & **Algorithm** & **PLCC** & **SRCC** & **KRCC** & **RMS** \\ \hline \multirow{4}{*}{FR IQA for retargeted images} & EL & 0.048 & 0.025 & 0.004 & 0.324 \\  & CL & 0.019 & 0.013 & \(-\)0.068 & 0.457 \\  & BDW & 0.062 & 0.050 & 0.046 & 0.274 \\  & BDS & 0.091 & 0.097 & 0.083 & 0.262 \\  & SIFT Flow & 0.149 & 0.151 & 0.145 & 0.238 \\ \hline \multirow{4}{*}{RR IQA for non-retargeted images} & DCT & 0.073 & 0.069 & 0.056 & 0.266 \\  & VIF & 0.185 & 0.187 & 0.168 & 0.232 \\ \cline{1-1}  & DNT & 0.161 & 0.153 & 0.137 & 0.241 \\ \cline{1-1}  & RRED & 0.253 & 0.247 & 0.234 & 0.225 \\ \cline{1-1}  & RRSSIM & 0.260 & 0.266 & 0.259 & 0.221 \\ \hline RR IQA for retargeted images & LEMD & 0.325 & 0.322 & 0.294 & 0.172 \\ \cline{1-1}  & GEMD & 0.129 & 0.126 & 0.120 & 0.236 \\ \cline{1-1}  & QS & **0.370** & **0.365** & **0.331** & **0.164** \\ \hline \end{tabular}
\end{table} 
Table 2: The performance comparison with different IQA algorithms.