## 4 Discussion and Conclusions  
We proposed a fully automatic no-reference quality assessment method for the enhanced images whose colorfulness, contrast, and sharpness were adjusted. HDR-derived features were obtained from HDR images with different scenes. Therefore, the proposed method without using the reference image with the same scene can be a 'no-reference' method. The proposed method does not require a reference image. It automatically calculates quantitative scores, visual preference, and quality assessment with respect to the level of colorfulness, contrast, and sharpness adjustment. This method considers colorfulness components. Additionally, this method uses HDR-derived features, which have more human visual preference than LDR images. By evaluating \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Basketball** & **Cactus** & **Traffic** & **Average** \\ \hline \multirow{2}{*}{NIQE} & SROCC & 0.6465 & 0.3233 & 0.1650 & 0.3283 \\ \cline{2-6}  & PLCC & 0.7144 & 0.3221 & 0.2373 & 0.4246 \\ \hline \multirow{2}{*}{CQE} & SROCC & 0.6207 & 0.4241 & 0.5292 & 0.5247 \\ \cline{2-6}  & PLCC & 0.6225 & 0.4236 & 0.5205 & 0.5222 \\ \hline \multirow{2}{*}{Proposed Method} & SROCC & 0.8042 & 0.9121 & 0.9669 & 0.8944 \\ \cline{2-6}  & PLCC & 0.7626 & 0.9284 & 0.9538 & 0.8816 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison between the conventional methods (NIQE, CQE) and the proposed method. Two difference correlation metrics called SROCC and PLCC were calculated between the scores measured by the automatic methods and by the subjective evaluation. Figure 7: Examples of test images with various levels of colorfulness, contrast, and sharpness adjustments. the LDR based on the HDR, this method can extract how different the quality of the test images differs from the HDR with a high visual preference and it offers visual preference scores, quantitatively. It shows that the proposed method yielded better performance than other methods. We investigated the performance of the proposed method depending on the size of image patches in global and local settings and measured the corresponding performance, according to the correlation values with subjective scores. In most cases involving our test image dataset, the highest correlation results were achieved with the sizes of image patches used in our experiments. Our proposed method currently used linear weighted coefficients trained by 576 HDR images. However, other machine learning metrics such as the support vector machine can be used in the training process. Because SVM-based quality evaluation methods have already been used in the detection on artifacts caused by compression [21; 22; 23; 24; 25], it is possible to use the SVM method for our proposed method. In addition, if there are additional features including the six features, neural network methods can be used for the evaluation of adjusted images. We made an effort to overcome the limitations of conventional methods such as NIQE and CQE methods. NIQE performs well in evaluating the distorted images due to compression, blurring, or noise corruption, but it is not focused on evaluating adjusted images. CQE can evaluate several adjustment effects such as colorfulness and contrast adjustments but does not incorporate HVS properties in its assessment process, which results in a mismatch with the subjective evaluations. These limitations were effectively overcome in our proposed method, which incorporated HVS-favorable HDR-derived features as standards in its evaluation process. HDR images have higher visual preferences for HVS than LDR images and, therefore, the features derived from HDR images are more closely related to the perception properties of HVS than those of LDR images. In addition to the incorporation of the HDR-derived features, both global and local features are extracted and combined to produce the final quality scores for an image assessment, which also considers the hierarchical visual perception mechanism of HVS.