A new iterative inverse display model

In this paper, we propose a new inverse model for a display based on the direct model developed in Kim and Lee (2015). We use an iterative method to compute what inputs are able to produce a desired color expressed in device independent color coordinates. Whereas iterative approaches have been used in the past for this task, the main novelty in our proposal is the use of specific heuristics based on the former display model and color science principles to achieve an efficient and accurate convergence. On the one hand, to set the initial point of the iterative process, we use orthogonal projections of the desired color chromaticity, x y , onto the display’s chromaticity triangle to find the initial ratio the RGB coordinates need to have. Subsequently, we use a factor product, preserving RGB proportions, to initially approximate the desired color’s luminance. This factor is obtained through a nonlinear modelling of the relation between RGB and Luminance. On the other hand, to reduce the number of iterations needed, we use the direct model mentioned above: to set the RGB values of the next iteration we look at the differences between color prediction provided by the direct model for the current RGB values and desired color coordinates but looking separately at chromaticity and luminance following the same reasoning as for the initial point. As we will see from the experimental results, the method is accurate, efficient and robust. With respect to state of the art, method performance is specially good for low quality displays where physical assumptions made by other models do not hold completely. • Our proposed method achieves notable efficiency and accuracy in determining input values for precise color reproduction. • The model’s ability to converge quickly while delivering highly accurate results suggests its strong potential for practical applications across areas like color management, image reproduction, and beyond. • Notably, our model performs especially well with lower-quality displays, where the physical assumptions used in other models may not fully apply. • It is robust in terms of convergence speed, maintaining consistent iteration counts across various colors and displays.