As LEDs move into general lighting developing high efficiency, optically uniform linear light sources is critical. Presented is a new linear light emitting optic designed to work with blue LEDs (450-460nm). This white appearance remote phosphor optic combines a high efficiency phosphor down-converter layer with an integrated secondary optic into a single component. The optic is produced using co-extrusion of two different optical materials into a single component.
The goal of ICC (International color consortium) color management system (CMS) is to reproduce color fidelity regardless of the hardware or platform used to capture, view or print them. The accuracy of profiles decides the precision of color conversion; therefore, creating device profiles accurately is very essential for color management. In this paper, according to the ICC standard format, we used Matrix-LUT (look up table) model, which can increase the color conversion precision to create monitor profile. In laboratory environment, we used X-Rite DPT92 to calibrate the monitor, and then we made about 1000 color patches and measured the RGB and the corresponding XYZ of each patch. We adopted linear interpolation method to establish the LUT between RGB and XYZ. The experimental results are good, and then we finished the monitor profile by the ICC format, realized CRT monitor color management.
KEYWORDS: CRTs, Visualization, RGB color model, Neural networks, Visual process modeling, Color difference, Reliability, Artificial neural networks, Eye, Data modeling
A visual method for characterizing cathode ray tube (CRT) displays and finding corresponding colors is presented. The method is motivated by the considerable expense of measuring instruments and the consideration of color appearance factors. In the experiment, a set of color chips in the natural color system (NCS) color atlas, an illuminant, and a CRT are used. 487 sample pairs of RGB (CRT) and HVC, which are the attributes of NCS color chips, are obtained by experimentally comparing softcopies and hardcopies in the office environment in the sense of best color matching to nine observers. Both the spectral data of the color chips and the colorimetric values of the CRT samples are measured. In addition, 12 error back-propagation (BP) neural networks are trained to help the realization of the transformation from HVC to RGB for data generalization. The current results show that the displays on the CRT can match the chips in color perception well.
Interest in color appearance models (CAM) has been greatly stimulated recently by the need in handling digital images. This article demonstrates that a multi-layers feed-forward artificial neural network with the error back-propagation algorithm was used to approximate color appearance model CIECAM02 with different white points and different media. For the prediction of the forward and inverse model respectively, in order to realize accurate mapping, especially to the inverse model, color spaces conversion between input color space and output color space (that is cylindrical coordinates and rectangular coordinates) was implemented before training the neural networks. Meanwhile we approximated the combination of the forward and inverse CIECAM02 models employing a neural network for different conditions including whites (D65 or D50) and media (booth and CRT) in order to realize the color transformation from one medium to another conveniently. The experimental results indicated that the prediction could satisfy the accuracy requirement. So in practice we can choose these two kinds of different prediction ways to meet our need according to different situations.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.