## 4 Conclusions  
The goal of tone mapping is scaling the dynamic ranges of original HDRIs as well as reproducing and preserving color in resulting tone-mapped images. A tone mapping method proposed in this article consists of the CCH-based TMO and CMCCAT2000-based CAT. The CCH-based TMO is a combination of the key value of the scene and the visual gamma and it enables the resulting, tone-mapped HDRIs to preserve and represent the detailed information. The purpose of the key value of the scene is to adjust contrast while the visual gamma is intended to control the dynamic range as well as avoid luminance shift and hue shift (color shift). The CAT processes the resulting tone-mapped images, thereby addressing the mismatch between the real-life scene and the displayed image and contributing to making the displayed image closer to the real-life scene. In the experimental results, the proposed method demonstrates better representation of the detailed information in the resulting images as well as reduced color shift, luminance shift, and color  
Figure 14: Preference score with 20 different images. leakage, compared to the conventional methods. In the CIELab color space-based measurement and comparison of color difference, the proposed method results in lower color differences than its conventional counterparts, which supports that the proposed method has improved in the color reproduction. In the colorfulness assessment and comparison conducted with the given, original HDRIs, the proposed method records higher values than its conventional counterparts except for a few images such as CWF and D65. In the hue measurement and comparison, the proposed method has larger hue values than the other methods. As a subjective evaluation, the psychophysical experiment is performed on 30 different images captured under five different standard illuminations and tone-mapped by several conventional methods and the proposed method in this article. As a result, the proposed method resulted in higher preference scores than its conventional counterparts. The proposed method proves to perform better than its conventional counterparts in several evaluations such as color difference formula, psychophysical experiment, and the like. We will continue to study and solve remaining problems in the timeline of the near future. Conceptualization, H.-H.C., B.-J.Y., and H.-S.K. ; Data curation, H.-H.C.; Formal analysis, H.-H.C. and B.-J.Y. ; Funding acquisition, B.-J.Y. and H.-S.K. ; Investigation, H.-H.C.; Methodology, H.-H.C., B.-J.Y., and H.-S.K. ; Project administration, B.-J.Y. and H.-S.K. ; Resources, H.-H.C. and B.-J.Y. ; Software, H.-H.C.; Supervision, B.-J.Y. and H.-S.K. ; Validation, H.-H.C. and B.-J.Y. ; Visualization and H.-S.K. and H.-H.C.; Writing--original draft, H.-H.C. and B.-J.Y. ; Writing--review and editing, B.-J.Y. and H.-S.K. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R11A3A01061844). This work was conducted as a part of the research projects of \"Development of IoT infrastructure Technology for Smart Port\" financially supported by the Ministry of Oceans and Fisheries, Korea. The authors have no conflicts of interest to declare. Equation  
Symbol(s)  
\(H_{X}\)  
\(h_{X}(m,\ n)\)  
\(p(m,\ n)\)  
\(\overline{p}_{v}\)  
Inverted PMF  
\(U_{X}\)  
Total number of nonzero items in \(H_{X}\)  
\(S_{v}(x,\ y)\)  
Corresponding image saliency  
\(S_{o}\)  
Saliency of image gradient orientation  
\(\Gamma_{e}(x,\ y)\)  
Edge-aware weighting  
\(\Gamma_{b}(x,y)\)  
Saliency-aware weighting  
\(\sigma_{L_{u},\ \rho_{2}}(x,\ y)\)  
Standard deviation  
\(\mu_{L_{u},\ \rho_{2}}(x,\ y)\)  
Mean value  
\(Y_{\text{hse}}(x,\ y)\)  
Base layer of image  
\(Y_{\text{detail}}(x,\ y)\)  
Detail layer of image  
\(\Omega_{p_{2}}(x,y)\)  
Overlapping window  
\(L_{\text{in},\ i}(x,\ y)\)  
Input luminance value  
\(\alpha_{i}\)  
Key value. \(\gamma_{v_{i},\ i}\)  
Visual gamma function.