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The 226 print products distributed as color difference intervals. The mean is 7.3 DE2000

The 226 print products distributed as color difference intervals. The mean is 7.3 DE2000

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Conference Paper
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This study is a continuation of a previous study on why most Brand Manuals fail when it comes to specifying Brand Colors (Pedersen 2016). This present study seeks to examine the practical consequences of the previous study by conducting a spot check of 226 randomly chosen print products from 43 randomly chosen companies. These print products where...

Context in source publication

Context 1
... limit should be raised to 8.2 ΔE 2000 for this study's products if 68% of the products should fall within an acceptance limit. In Figure 6 and 7 the distribution of the color differences shows that most products had a color difference between 2 and 10 ΔE 2000. Approximately half of the products had a color difference above 6 ΔE 2000 and 6 products had a ΔE 2000 between 21 and 34 (these might be considered as outliers and are removed in figure 7). ...

Citations

... In many cases, they choose colors that are bright and chromatically saturated. However, most of these brand colors cannot be reproduced in printing technologies using the process colors CMYK (Pedersen, 2016;2018). In addition, many of these colors cannot even be displayed on RGB screens, which a "Gamut Warning" attempts to warn the designer about in Adobe CC. ...
... As previous studies have shown, designers and brand owners tend to choose brand colors that cannot be reproduced satisfactorily in CMYK (Pedersen, 2016;2018). It is also a well-known fact that computer screens do not always display colors correctly, so what will happen if colors increasingly are selected directly from what is seen on a screen? ...
... In Brand Color Management, which hereby is introduced as a new concept, the four traditional Cs of color management are expanded to nine Cs, thus including the entire color selection process also including the decision on which acceptable color differences the brand owner will be willing to accept − or have to accept, as described in previous studies (Pedersen, 2016;2018). Thereby, all the process, from the initial color choice to the final consequences of this color choice is covered. ...
Article
Full-text available
Published in Journal of Print and Media Technology Research, Vol. 11 No. 2 (2022), pp. 85-97. (For full text, just click on the DOI-link) This paper examines some of the implications of choosing Brand Colors directly from a software program, based on what the designer see on the screen. The reason is that more and more Graphic Arts Designers tend to choose colors directly from the screen and to a lesser extent from a physical color catalog such as the Pantone fans. Therefore, designers do not really know what color they have chosen until it is available in the final printed form and this can bring unpleasant surprises. The starting point for this study is the digital solutions from Adobe CC and the web service Pantone Connect. The focal point is sRGB, as Pantone recommends, which also is the standard for Internet and mobile devices. Initially, the problem with using a small color space as sRGB to select and define Brand Colors is investigated. Examples of Pantone colors outside sRGB gamut but still available for the designer to choose without any warnings, have been sought. For example, if the designer chooses Pantone Green C as a Brand Color, the result would be a color difference of 10.5 DE2000. The CIELAB values for a color defined in sRGB are compared to the CIELAB reference values for the same color as it will appear as a printed as spot color. This gives a color difference (DE2000) between how the color appears on the screen versus the physical color as it appears on a print, printed as 1-color solid spot color. Pantone Connect's feature for converting colors from sRGB to Pantone Solid spot color is also investigated. As an example, entering sRGB values for cyan (0/255/255) results in a proposal for Pantone 311C as Best Match, giving a color difference of 17 DE2000 between the reference values for Cyan and the reference values for the proposed Pantone 311C. In addition, the feature to extract color code values from an uploaded photo is examined. An iPhone screenshot image of Pantone 1505 C (orange) is uploaded to Pantone Connect, which interprets the color as being Pantone 1585 C giving a color difference of 6.3 DE2000. The overall conclusion is that it is very uncertain and unpredictable to choose colors directly from a screen. The color differences between the color as it appears on the screen and the color as it appears on the final print can be very large and thus be the cause of the customer's dissatisfaction, even if the customer is partly responsible. As a consequence of the lack of management and control in this area, Brand Color Management is introduced as a new technical concept. It is an extension of traditional color management, so it also includes color selection, color specification, color description and an extended form of Color Control.