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Color management for digital imaging systems
Published in Sharma Gaurav, Digital Color Imaging Handbook, 2017
Edward J. Giorgianni, Thomas E. Madden, Kevin E. Spaulding
All successful color imaging systems employ some form of color management. Color management can be defined as a means for predicting, controlling, and adjusting color information throughout the system — from the initial color capture to the formation and display of output images. In chemical and other analog-based imaging systems, color management may be implemented in various ways, including equipment calibration, chemical process control, and operator-controlled or automated color-printing adjustments. In digital imaging systems, color management is generally implemented using software designed specifically for that purpose. The principal function of that software is to process (transform) image signals derived from an input device to make them appropriate for a given output device. Digital color management can be relatively simple when applied to imaging systems that are restricted to only certain types of inputs and outputs, but, when applied to systems having a variety of different types of input and output devices and media, color management can become quite complex.
Artificial neural networks approach for prediction of CIELab values for yarn after dyeing and finishing process
Published in The Journal of The Textile Institute, 2023
Cenk Şahin, Onur Balcı, Melek Işık, İlker Gökenç
The key to staying on top of fashion trends in the textile industry on a timely basis is having a reliable color management system. The process of color management starts with the preliminary preparation of the material and continues all the way to the finished product stage, during which the material reaches its completion stage. The most critical part of color management, recipe matching, can be approached in two methods. The first method does not rely on any analytical methods at all and is completely carried out with the help of practitioner’s experience. A second, more common method is based on the Kubelka-Munk theorem, in which color matching features in spectrophotometers are used. In this method, the dyeing recipe is formulated on the basis of the substrate’s readiness for dyeing and the dye’s expansion set. However, other factors such as fiber, yarn, finishing processes, etc., which are known to affect color, are not considered in color matching. If these details are necessary for managing a color system, the practitioner’s experience is reflected in the result (Balcı & Oğulata, 2009).
Constructing physical space design for high color gamut in mixed reality environment
Published in Journal of Information Display, 2023
Jihyung Kim, Jonghyeon Ka, Ju Hong Park, Wooksung Kim
Commercialized OST-HMDs chose to reduce the illumination of the external environment in which the device is used to solve the display visibility reduction phenomenon. For example, for the Microsoft HoloLens2, they added shading to the front of the HMD, and for the Epson Moverio series, they provided a black-coated filter. This solution of changing or adding physical structures could temporarily improve display performance. However, the rather bulky OST-HMD form factor issue caused user discomfort. Also, this method has a disadvantage – switching meaningful display performance according to the usage environment is impossible. Researchers from universities and institutes have proposed various theoretical solutions. Ramos et al. proposed an algorithm for real-time color correction based on the display profile [19]. They presented a middleware called Smart Color for color management of interface components and used a method to output alternate colors that best preserves the original colors we want to output from the display. As a result, the readability of the text output within the display has increased significantly. However, this method has only been demonstrated in a simulated environment and is not a solution applicable to real products as it does not use a camera to collect usage environment information for the OST-HMD.
A study of the effects of fabric pretreatment on color gamut from inkjet printing on polyester
Published in The Journal of The Textile Institute, 2018
Yi Ding, Lisa Parrillo-Chapman, Harold S. Freeman
A digital image consists of a number of ‘pixels’, which is the smallest controllable element used to represent an image. Similarly, during the printing process, tiny drops of each ink can be jetted onto fabric as a matrix, such as a 4 × 4 or 6 × 6. The matrix is also referred to as a ‘super-pixel’, and the number of drops and the volume of ink delivered to each location within the super-pixel has a controlling influence on the shade depths (gray levels) produced. Figure 4 illustrates how to achieve 17 different gray levels in a 4 × 4 matrix, by increasing ink drop coverage in 17 steps. In general, the total average number of drops applied per pixel has a linear relationship with the corresponding gray levels in the pattern data, which is a continuous visual change in color shade. However, the visual color strength of the printed fabric is not simply related to the number of drops per pixel. Color measurements are used to characterize changes in color yields upon increasing drop coverage in the super-pixels. As the gray level increases, there will be color yield for some of the inks, indicating that there is not much change in shade depths with increasing drop coverage. At this point, increasing the amount of ink applied simply results in increased penetration of the color into the fabric. Therefore, modification of color calibration and color matching is required for different types of printing environments. This modification is conducted by color management software (CMS), which attempt to ensure that individual color printers are calibrated and produce shades matching those originally specified by the end user (Allen & Triantaphillidou, 2012).