###### Abstract  
Color visualization on websites and in apps usually tacitly assumes device-independent sRGB color space, bypassing the essential step of characterization--a process that requires expensive equipment and significant time. This work proposes a pseudo-device-independent approach applicable to Apple displays, one of the most renowned brands in this area, eliminating the need for characterization by leveraging the Mobile Display Characterization and Illumination Model (MDCIM). For this purpose, 11 Apple mobile displays were characterized, analyzing variations in parameters from spectroradiometer measurements and improving their sRGB tone rendering curves. Tested on an iPhone XS Max, the Color for the Average Apple Display model (CAAD) improved reasonable to good color matches from 4.0% to 84.3%, while incorrect matches dropped from 85.3% to 4.9% when compared to sRGB. These results are nearly as strong as those of the device-specific MDCIM, suggesting that an average model is a practical alternative to individual display characterization. c 20251569151 2025156915 2025156915 2025156915 2025156915 20251569  
On the one hand, it may not be sufficient to rely on a color space like sRGB [2], as it was originally developed to meet the chromatic specifications of now-obsolete cathode-ray tube monitors. For example, Figure 1 illustrates the color gamuts of two displays used in this study, one based on LCD technology and the other on OLED. As shown, their gamuts differ not only from each other but also from the standard sRGB gamut. This highlights the importance of developing new approaches for color rendering in modern display technologies. On the other hand, if the display is part of a mobile device, ambient lighting conditions also need to be considered for improving color reproduction accuracy. Mobile displays are typically used under a wide variety of ambient lighting conditions [3]. Even for indoor office applications, illuminance levels may vary considerably, with typical values between 500 and 2000 lx [4]. The optimal color representation on a display is highly affected by the level of ambient lighting. Changes in ambient illumination can alter the viewer's perception. In addition, when referring to displayed colors, the spectral power distribution (SPD) of the display's light source significantly impacts color rendering. The type of display technology also affects how colors are perceived. Moreover, color perception is inherently subjective and context-dependent, influenced by factors such as surrounding colors, observer adaptation, and individual differences in visual sensitivity. Due to this subjectivity and the many variables involved in color perception, it becomes essential to understand the visual response to rendered samples through properly collected data obtained from well-designed and validated psychophysical experiments. Different display calibration methods are commercially available. However, most users do not have the time or expertise to use those solutions. They can be grouped into three main categories: LUT-based models, numerical models, and physical models [5; 6; 7; 8; 9]. LUT-based models use dense measurements and interpolation to map RGB values to a colorimetric space (e.g., XYZ). They are highly accurate and handle RGB channel interdependence well but require large datasets and are resource-intensive. Numerical models aim to approximate the RGB-to-colorimetric mapping using algorithms such as polynomial regression or neural networks. Polynomial models are simple and computationally efficient, but struggle with complex or non-linear device behaviors. Neural networks, by contrast, can model highly non-linear relationships and adapt to complex displays, though they require large datasets, are computationally expensive to train, and lack interpretability. Physical models are based on device physics, typically involving an electro-optical  
Figure 1: Color gamut from sRGB encoding color space compared to the gamuts of iPad 2 (LCD) and iPhone 12 (OLED) displays.