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Getting the Most from Simulated-Process-Color Reproduction

(August 2009) posted on Fri Aug 07, 2009

Coudray discusses the influences that ink values, software, and other variables have on the creation of effective simulated-process separations.


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Simulated-process reproduction offers so many advantages compared to other methods, particularly the fundamentally unstable four-color process, that there is really not much of an argument anymore for using it in favor of other techniques. But that doesn’t mean that simulated process isn’t without its challenges and limitations. This month I’d like to look closely at some of the fundamental issues printers face when they choose to use this approach to full-color reproduction.

Is it the seps?
At the very front of the process is the question of whether your simulated-process separations are adequate or not. Like traditional four-color process, any halftone method is a simulation of what the eye sees and the brain processes. Simulated-process or nChannel separations, where n represents the number of printed colors, physically limits us to replicating tone and color variation through the use of various dot sizes or the number of dots in a give area. There are dozens of ways to make nChannel separations, and they vary from terrible to incredible.



Most of the common approaches use Adobe Photoshop and involve RGB to CMYK conversion and then manipulation of both the RGB and CMYK channel data through scripting, color-range extraction, channel masking, layer masking, and channel calculations. Lesser approaches, such as inversions, difference calculation, and filter enhancement, also may be used.

The more sophisticated methods involve nChannel or nColor profiles, in-RIP separation of an RGB image, or separation through dedicated software that’s designed specifically for the purpose of nChannel color mapping. These methods deliver extremely accurate and excellent separations, but they are considerably more expensive than using Photoshop or a Photoshop plug-in. All of the Photoshop approaches have one thing in common: they’re pretty much guess work based on the skill of the operator. Don’t get me wrong—some amazing separators out there can work Photoshop magic with these methods. But that still does not change the fact they are using their experience and judgment to create grayscale channel data that will deliver a good to great nChannel result.


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