# Contrast Sensitivity Based Multiscale Base-Detail Separation for Enhanced HDR Imaging
## 5 Simulations
Subsequently, we perform the objective evaluation of three aspects, sharpness, HDR rendering quality and image quality using 15 HDR images in Figure 8. For the sharpness evaluation of each method, we select four kinds of sharpness metric, spectral and spatial sharpness (S3) [17], cumulative probability blur detection (CPBD) [18], local phase coherence-sharpness index (LPC-SI) [19] and just-noticeable blur (JNB) [20]. S3 perceives the local sharpness in image using spectral and spatial properties, and it was validated by comparison with sharpness maps generated by human subjects. CPBD is based on a probabilistic framework on the sensitivity of human blur perception at different contrasts. It is evaluated by taking into account the HVS response to blur distortions and the perceptual significance of the metric is validated through subjective experiments. LPC-SI evaluates image sharpness according to the degradation of LPC strength which is influenced by blur. JNB is defined as the threshold in which humans can perceive blurriness around an edge shown in a contrast higher than the just-noticeable difference. JNB considers the response of the HVS to sharpness at different contrast levels. The above methods do not require a reference to evaluate. The higher score of each method represents the properly sharpened image. In particular, S3, CPBD, and JBN methods are based on HVS simulation and the results of each method well-reflect the visual characteristics. Table 1 shows the average sharpness score of four tone mapping methods. The proposed method has higher tone mapping than other methods for each sharpness method. Figure 10: HDR rendered images (light box). (**a**) [PERSON] (2012), (**b**) [PERSON] (2018), (**c**) iCAM06 and (**d**) proposed method. Figure 9: HDR rendered images (bookshelf). (**a**) [PERSON] (2012), (**b**) [PERSON] (2018), (**c**) iCAM06, and (**d**) proposed method. In Table 2, we compare a rendered HDR image quality in each method using the tone mapped image quality index (TMQI) which is an objective quality assessment metric for tone-mapped images [21]. TMQI consists of a multiscale signal fidelity measure and a statistical naturalness measure. The fidelity measure extracts the structural information from the visibility to estimate the perceptual quality. It calculates the cross-correlation between HDR and LDR image pairs, and uses the pyramid image to evaluate the visibility of image detail according to the distance between the image and observer. The naturalness measure evaluates the brightness and contrast of the tone mapped image. The TMQI score is the weighted sum of the fidelity measure and naturalness measure. Each measure ranges from 0 to 1 and the larger score indicates better quality in image rendering. Our test result is shown in Table 2. The fidelity measurement uses local standard deviations for the structural information. When the structural information of the tone mapped image is close to that of input HDR image, the fidelity score is higher. However, the proposed method compensates for the local details using relative CSF gains in frequency domain. Therefore, the local standard variations of the proposed method are higher than those of the iCAM06. As a result, the fidelity score of the proposed method is slightly lower than iCAM06, but the naturalness score is better than those of other methods. The final TMQI score shows that the proposed method has the highest score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **[PERSON](2012)** & **[PERSON](2018)** & **iCAM06** & **Proposed** \\ \hline TMQI & 0.839 & 0.826 & 0.866 & **0.868** \\ Fidelity & 0.874 & 0.870 & **0.898** & 0.886 \\ Naturalness & 0.264 & 0.204 & 0.361 & **0.387** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with TMQI scores. Figure 11: HDR rendered images (wood). (**a**) [PERSON] (2012), (**b**) [PERSON] (2018), (**c**) iCAM06 and (**d**) proposed method. In Table 3, we compare image quality for each method using a no-reference perception-based image quality evaluator (PIQE).