Monthly Archives: September 2013
The color difference, or ΔE, between two colors: L1a1b1 and L2a2b2 is:
|1931 CIExy Chromaticity Diagram|
When I conceived this idea, I had just brushed the surface of color knowledge. I soon found out that few spectrophotometers or colorimeters capture the colorimerty data from the emitted light on a display device. Display characterization devices, such as the X-Rite i1 measures the light to calibrate, but provide no numerical color data. Such devices do not output the spectral data. After many inquiries to X-Rite and waiting for the engineers to return my call, I finally got an answer about SpectraShop 4, software program that would not only measure, but provide a report of measured colormertic data. SpectraShop 4 is a great piece of software. I think that it has great potential for future use of color measurement needs.
This study has practical application for mobile device manufacturers. Creators of content for mobile devices would be the other group of people who would benefit from this research as users of the content. The industry needs to educate the consumers who might make important color decisions using a mobile device that does not accurately reproduction the original image’s color correctly.
The problem is a current issue that needs to be solved. The population affected is overall is huge and includes all countries that have mobile devices. People who own smart phone are usually innovators and influence others. This is important to understand because the mobile device market is only going to increase. Industries that will be effected include graphic design, publishing, medical testing, food quality and most sciences. Anyone who makes a decision based on color from a mobile device would benefit from the finding and conclusions of this study.
This study’s problem stems from the relationship of the mobile display’s technology and how this technology accurately renders color. The dependent variable is control image. The causal or independent variable is the sample of current mobile devices and is responsible for the change in problem variable. The control image is a function of the mobile device and the differences will be examined. If there is a change in causal variable (as a change in device), then there is an expected change in the problem variable.