## 5 Conclusions  
This study offers a promising approach to leverage the advantages of the MDCIM: the development of a generalized model that, while potentially not suitable for some devices, could be effective for a range of devices with shared characteristics. Notably, it is demonstrated that on the iPhone XS Max, under specific lighting conditions, the device-specific MDCIM delivers almost identical results to an averaged model derived from the characterization of 11 Apple devices, the CAAD. These findings pave the way for creating less device-dependent MDCIMs, simplifying the characterization process, and enabling more accessible implementation without significantly compromising color reproduction accuracy. This article explores the effectiveness of the MDCIM [10], which adapts the GOG model to incorporate both ambient lighting levels and the chromatic properties of the device on which it is used. The model has been tested across a wider range of devices, and the results demonstrate that its parameters typically differ from those of the sRGB space (\(\gamma_{i}=2.4\)). This study presents the results for 11 different mobile and tablet screens. Using measurements from a spectroradiometer, the MDCIM parameters were specifically optimized for each screen, making it possible to examine how these parameters vary across devices. The results of a visual experiment conducted on a specific OLED display, the iPhone XS Max, are also presented. Using averaged parameters from all Apple devices for on-screen color reproduction yields nearly the same accuracy as using device-specific parameters. This indicates that achieving a substantial improvement in color reproduction accuracy with the MDCIM does not necessarily require fully device-specific parameters. This fact is noteworthy, as it would allow for improved color reproduction on a display without requiring full characterization measurements with a spectroradiometer, a process that can take between 30 and 60 min, depending on whether the device is an LCD or an OLED. OLEDs, which produce deeper dark colors, emit less light than LCDs, thus requiring longer integration times for measuring the luminances of most images in the characterization. By having predefined \(\gamma_{i}\) and \(d_{0,i}\) parameters applicable to a range of models, the MDCIM could be implemented to enhance color reproduction on any device. Additionally, it would be necessary to know the chromatic coordinates \((x,\,y)\) and the maximum luminances \(Y_{max}\) for the three device channels. This information is available online through video diagnostic platforms like DISPLAYMATE [34]. Future research could explore additional visual experiments to directly compare the two MDCIMs (specific and average) with the sRGB color space, assessing their performance under various lighting conditions. This would help to gain a deeper understanding of how they perform in different lighting environments. It would be especially useful to examine the impact of the device-specific chromatic characteristics on color reproduction accuracy, as well as the role of adapting to ambient lighting conditions. This information could help inform the development of a model that places less emphasis on device-specific features and focuses more on adjusting to ambient light. The CAAD model incorporates two key elements: the use of a device-dependent color rendering and the inclusion of ambient lighting conditions. Nonetheless, the inclusion of ambient lighting conditions is a particularly significant factor in the performance improvement, as it allows the model to better reflect the perceptual context in which colors are actually viewed. In the future, it would be of interest to explore whether a device-independent model could yield results comparable to those of device-specific models acrossvarying lighting conditions. If such a model proved effective in most scenarios, it could greatly simplify device characterization and facilitate broader adoption. Investigation, A.L., E.K., L.N., E.P., C.V. and J.E.