# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 5 General Discussion
### Impact of the Diffuse White Luminance  
Our results suggest that the best color space for assessing the quality of HDR images depends on the test database. Indeed, some of the color spaces are adapted and tuned for one visualization condition. The \(HDR\)-\(Lab\) color space considers two important parameters, i.e., the diffuse white and the surround luminance. Moreover, the final equation of the \(j_{2}a_{2}b_{2}\) color space (Equation (13)) was  
Figure 12: SROCC performances for the Narwaria et al. database for color-blind quality metrics (**a**) and for color quality metrics (**b**). tuned using the experimental dataset called SL2 [20]. This dataset was obtained for a diffuse white at 997 cd/m\({}^{2}\). This explain why the \(J_{z}a_{z}b_{z}\) luminance have a behaviour close to the \(HDR\)-\(Lab_{1000}\) luminance (cf. Figure 2). The PU function and the \(ICtCp\) color space were not obtained through the same kind of training. They were created using [PERSON]'s Contrast Sensitivity Function model [51] and [PERSON]'s Contrast Sensitivity Function model [52], respectively. However, Figure 2, that represent the different color spaces luminance in function of the linear luminance, suggest that \(ICtCp\) and the PU encoded luminance have a behaviour closer to the \(HDR\)-\(Lab_{100}\) luminance than from the \(J_{z}a_{z}b_{z}\) luminance or than the \(HDR\)-\(Lab_{1000}\) luminance. Because the color spaces are adapted for different viewing conditions, it is not easy to determine the best color space. * With the proposed database 4 Kdtb, the color spaces with a diffuse white around 100 cd/m\({}^{2}\) (\(ICtCp\), \(HDR\)-\(Lab_{100}\) and the PU-mapping) give better performances than \(J_{z}a_{z}b_{z}\) and \(HDR\)-\(Lab_{1000}\) spaces. We also observe that the performances of color metrics are more sensitive to the color space choice. * We draw a similar conclusion on [PERSON] et al. database, except for FSIM and FSIMc (cf. Figure 9). These two metrics are less sensitive to the color space for this database. * With the proposed database HDdtb (cf. Figure 11), the \(J_{z}a_{z}b_{z}\) color space provides the lowest performances for PSNR, SSIM, MS-SSIM and PSNR-HMA metrics but provides the highest performances with FSIM and FSIMc. However, results indicate that the PSNR, SSIM and PSNR-HMA metrics based on \(HDR\)-\(Lab_{1000}\) and \(HDR\)-\(Lab_{100}\) color spaces perform better than the same metrics using the \(J_{z}a_{z}b_{z}\) color space. This suggests that the low performances of these metrics is not due to the diffuse white characteristics of the images, but to the design of \(J_{z}a_{z}b_{z}\) color space which corrects a deviation in the perception of the blue hue (cf. Equation (9)). To test this hypothesis, we measure the SROCC of these metrics on the HDdtb database with the \(J_{z}a_{z}b_{z}\) color space without the blue deviation correction. We call this new space \(\widetilde{J_{z}a_{z}b_{z}}\). Results, shown in Table 5, indicate that SROCC values of the three aforementioned metrics increase with the \(\widetilde{J_{z}a_{z}b_{z}}\) color space. In addition, metrics using this modified color space provide similar performances to metrics based on the \(HDR\)-\(Lab_{1000}\) color space. This is consistent with the fact that \(HDR\)-\(Lab_{1000}\) and \(J_{z}a_{z}b_{z}\) are adapted to almost the same diffuse white luminance. This might be due to the presence of the \"gammut mismatch\" artifact in this database. Indeed, the \"gammut mismatch\" artifact creates visible distortions that was not associated with a subjective quality loss during our test. We suspect that the blue hue deviation correction makes the \(J_{z}a_{z}b_{z}\) color space more sensitive to this distortion. However, this is difficult to demonstrate due to the low number of images with this kind of artifact present in this database. * With the [PERSON] et al. database, it is difficult to draw a conclusion (cf. Figure 12). The MS-SSIM and the SSIM metrics perform better when using the \(J_{z}a_{z}b_{z}\) color space.