KEYWORDS: Computer programming, Color reproduction, Signal processing, Image processing, Chemical elements, Data conversion, Cameras, RGB color model, Zirconium, Ytterbium
Multiprimary displays are needed to reproduce the most of visible color. However, while using four or more primary colors, it is difficult to convert color uniquely, because multiprimary displays have variability of color conversion. Even though multiprimary colors can be converted, there are some false edges in the reproduced image by using the most of the current conversion methods.
We introduce linear programming to the color conversion in order to get high accuracy and eliminate false edges. Although the linear programming can help multiprimary displays to reproduce images without false edges, it takes long time to convert high-definition images. Hence, a fast color conversion method that keeps the advantage of the linear programming is proposed. This method is constructed of decision tree and linear programming. Because the decision tree is a discrete classification method, color conversion by the decision tree must be implemented as a discrete classification problem. Therefore, we feed the results of the conversion by linear programming to the decision tree. As the result of the conversion by the method, fast color conversion was obtained in comparison with that obtained only by using linear programming. In addition, our method almost eliminated the false edges in the reproduced image.
An image capturing system for the reproduction of high-fidelity color color was developed and a set of three optical filters were designed for this purpose. Simulation was performed on the SOCS database containing the spectral reflectance data of various objects in the range of wavelength of 400nm ~ 700nm in order to calculate the CIELAB color difference ΔEab. The average color difference was found to be 1.049. The camera was mounted with the filters
and color photographs of all the 24 color patches of the Macbeth chart were taken. The measured tristimulus values of the patches were compared with those of the digital images captured by the camera. The average ΔEab was found to be 5.916.
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