Saturday, August 25, 2018
Tuesday, August 21, 2018
State of the art in Extended Gamut printing
I had a series of blog posts on expanded gamut (parts one, two, and three) which were very popular. When I say "very popular," I of course mean that I have indirect evidence that there may have been one person -- possibly in Spokane, WA -- who stayed on the web page for longer than one minute. While it is likely that he or she was not actually looking at the screen at the time, we cannot dismiss the possibility that someone actually read part of one of the previous blog posts.
Obviously, I need to follow up, to provide some practical advice on expanded gamut. Since I am only capable of impractical advice, I have called on a friend who has convinced me that he knows stuff about this stuff. I turn this over to today's guest blogger, Mike Strickler.
Introduction
John has treated us to an entertaining history of attempts to achieve more colorful results by overcoming the limitations of 4-color printing. But what of the situation today? What does “Extended Gamut” mean in the present context; what do these solutions look like? EG systems now range from simple arrangements consisting of nothing more than Photoshop and a multichannel output profile to entire integrated workflows with proofing and elaborate options for spot color handling. But for all the recent attention paid to the subject, there is still a lack of industry consensus on just what a constitutes a proper EG separation, and any effort to make sense of the subject typically faces a mixture of conflicting proprietary claims, incomplete studies, and persistent misconceptions. It is still the Wild West. Perhaps by considering how these systems came to exist we might better understand what they actually do and how well they fulfill their purpose.
Extended-gamut today: a dual heritage
We can simplify the origins of all EG systems to two distinct lineages, each corresponding to a different need: Systems that convert images and those that convert spot colors. Combining and reconciling these two functions is the key challenge faced by designers of EG systems today.
Spot color conversion systems: a tyranny of rules
As we read earlier, several decades ago some clever individuals saw that one might reproduce a wide range of spot colors as equivalent process builds using CMYK with the addition of secondary inks such as a red, orange, green, blue, or violet. This was in the days before digital color management, so lookup tables were laboriously built from trial press runs and served as guides for converting colors, object by object. These systems mostly used AM screening, so immediately the question of screen angles had to be addressed. The usual scheme of 0, 15, 45, and 75 degrees was seen as imperfect because it forced a choice of either creating additional intermediate screen angles, increasing the risk of moirĂ©, or sharing screening angles among complimentary colors such as cyan and red/orange and magenta and green, an unpopular choice as it raised the risk of color shifts caused by misregistration on press—a reasonable concern when printing vector objects, with their well-defined boundaries. A near-consensus converged around two simple rules for multicolor separations:
1. No color shall be converted to more than 4 process colors. Three is even better.
2. Complimentary overprints, such as cyan-red/orange and magenta-green, must be prevented. Who needs them, anyway?
These rules continue to be applied in a majority of EG systems, even those updated with automatic color-managed conversions. Spot color tints and overprints are handled by arithmetic interpolation or other simple means. The intricacies, and limitations, of these schemes will be discussed in the next blog. For the remainder of this one, we’ll focus on image conversions.
Images conversion systems: the rise of multichannel ICC profiles
While those brave pioneers were building their lookup tables, deft prepress workers were enhancing CMYK images with “touch” or “bump” plates of stronger colors. This practice can be seen as the true progenitor of multichannel extended-gamut systems.
With rare exceptions, modern image conversion methods rely on ICC color management. For generating multicolor (CMYK+N) press separations a basic system includes two profiles, one for the source color apace (often RGB) and one for the multicolor destination space, a CMM (Color Matching Method), AKA a “color engine,” and an application such as a RIP, color server, or other color-managed program such as Photoshop to interpret the source pixels and build the new image.
The profiles contain lookup tables that translate color appearance values (XYZ or Lab, AKA the PCS, or Profile Connection Space) to device values and vice versa. The CMM draws smooth curves through the LUT points and interpolates all intermediate values. PCS to device (or B2A) tables may contain a good deal of “secret sauce” for enhancing printability, saving ink, increasing the amount of black in neutrals, etc.; separate tables are built for different rendering intents or gamut mapping strategies. On top of this, device-link profiles may be employed to bypass the PCS conversion and apply even more rules, e.g., to exempt certain colors from the conversion. The key points to know about multicolor ICC systems are:
They are automatic, fast, and precise.
They are able to juggle multiple objectives, the two most important being accuracy and smoothness of output color, even up to 7 or more output channels.
Any combination of output channels may be used to fulfill objectives.
Results are conditioned by the quality and completeness of underlying measurement data.
Two systems, two outcomes
Image conversion is still an important function of EG, and systems do perform differently depending on their underlying logic. To illustrate this we can look at the results from converting an RGB test image with two very different solutions, one developed primarily for simulation of spot colors—you might say an heir to those clever lookup tables—and the other a typical representative of ICC color-management, the current default method for converting images. The test image contains a smorgasbord of truly devilish color conversion challenges: delicate flesh tones, textures and smooth gradations in deep, saturated colors, and full-tone black and white images. Any serious defect in a color conversion system is unlikely to escape detection here.
Details: Some gamut compression will be required convert these images to these smaller multicolor output spaces: In the first example perceptual rendering intent was used; in the second, relative colorimetric with black point compensation was chosen as the best available option with that system. Black generation (GCR) was adjusted to be as similar as possible in both cases.
System A. This is a conventional ICC-based system, available as a standalone profiling application (to be used in ICC-compliant workflows) and optional color server, which was employed here for the conversion. The underlying measurement data of the profile (for coated packaging board) is plotted in Lab space below. It shows a good balance of shadow, mid-tone, and highlight samples. A modest number of complementary color pairs (C-O, M-G) and extra-color overprints (O-G, V-G, OV, OVG) are present.
The results, shown below, are good. Details and tonal separations are preserved in the deepest and most saturated colors; gray balance is excellent, and no contouring, banding, or posterization is evident. Gradations are smooth.
The OGV separation view below shows a long scale for the OGV channels; they extend deep into colors that are printable with CMYK alone (flesh tones, underside of sunflower).
The graph below, derived from the lower-right image of the jack-o-lantern plant, shows how a range of colors from red-orange to dull green is composed. The extensive interleaving of channels, including small amounts of complementary colors, is a very probable contributor to the visual smoothness of the color transitions.
This system would be a good choice for demanding image reproduction work.
System B. Here we have a non-ICC system sold as an option for a popular workflow suite. It is a bit of a hybrid, its profiling scheme showing echoes of earlier simple spot-color lookup systems: Complimentary colors (C-O, M-G), extra colors (O.G.V) do not overprint, and no build exceeds 4 total colors. Otherwise, its profiles contain an abundance of tint and overprint data, as seen in this Lab plot:
The profile structure is unique, consisting of 4 4-color charts, thus simplifying its design. As we’ll see, simpler is not necessarily better, as when approaching multidimensional problems like image transforms in 7-color space.
The converted test image below predictably shows some differences with Sample A. Color transitions are more abrupt, as shown clearly in the patch chart at the top of the form.
The OGV separation view shows very limited replacement of in-gamut CMY by OGV inks, though this reportedly may be adjusted to some degree in the software. Gradations in these channels are noticeably less differentiated than in System A. The overall appearance of the n-channel separations is more like that of old-fashioned touch colors than fully functioning process channels. The red-green transition contains much less channel interleaving than in Sample A.
Two image comparisons will suffice to show some implications for these different separation schemes. In the lower area of the still life image below we see the dramatic impact of the compacted tonal scale of the violet channel in System B (right). Deep blues are hollowed out and posterized. Faithful rendering of dark, saturated colors is a critical attribute of any good color reproduction system, and this example is decidedly sub-par. System A (left) shows normal results.
The next image detail is a good test for reproduction of subtle color transitions. As seen in the System A image (left), the jack-o-lantern displays a nicely nuanced transition from orange to yellow-green in which a multitude of slightly varying flecks of yellow-green and yellow-orange can be seen. The System B image (right) looks comparatively crude, with a relatively flat, featureless orange abruptly breaking to an undifferentiated yellow-green. (This may be difficult to see in this low-resolution image.) Such defects are not correctable through image retouching, as the separations simply lack the necessary supporting tonal information.
This image also shows an interesting feature of System A. In the transitional color regions we see the presence of the complementary orange-cyan combination:
These colors, as well as green and magenta, are of course mutually cancelling and therefore commonly regarded as unnecessary in a press separation, a belief so widely accepted that it might be regarded as one of the tenets of extended-gamut printing. As noted earlier, such combinations are excluded from the separation in System B. However, closer study reveals a useful role for these “interstitial” colors in smoothing transitions.
A cautionary note on profile accuracy
An otherwise capable multicolor system may be compromised by the sort of measurement data underlying the profile being used. Here is a Lab plot of a typical 7-color measurement set used by a popular ICC profiling application.
Notice the extreme abundance of dark overprints, far in excess of any possible utility, and the skeletal representation of mid-tones and highlights. This gaping void must be interpolated, literally guessed at by the CMM. The resulting converted images are smooth, but intermediate values are largely fictitious. The less linear the device output is the worse the fiction! There is no known workaround with this application.
Conclusions
Modern multicolor extended-gamut systems must capably convert a variety of elements, including images, vector objects, and spot and process colors, singly and in combination. Systems based on older spot-color matching schemes may have a tougher time converting images compared with systems based on ICC multichannel profiling. Nonetheless, these older systems continue to enjoy popularity in package printing, where the dominance of vector designs gives rise to concerns about printability that may compete with the need for high image fidelity. In the next blog we’ll see how these two schools of thought play out in actual practice, with particular focus on strategies and techniques for converting spot colors. If space allows we’ll also touch upon the arcane subject of EG proofing.
Are you having a multicolor crisis of your own? Are battles over your system configuration leaving you black and blue? Please feel free to post comments and questions here, or send them to mike@mspgraphics.com.
About today's blogger
Mike Strickler is the guy I call on the rare occasions when I want to sully my mind with practical concerns about color management. Mike is a specialist in graphics arts and color management. He is an Idealliance G7 Expert, CMP Color Management Master Trainer and member of PIA and IAPHC. Based in the San Francisco Bay and Los Angeles areas. Specialties: Color management, printing, remote proofing, and photography.
Mike is the principal at MSP Graphic Services.
Obviously, I need to follow up, to provide some practical advice on expanded gamut. Since I am only capable of impractical advice, I have called on a friend who has convinced me that he knows stuff about this stuff. I turn this over to today's guest blogger, Mike Strickler.
Introduction
John has treated us to an entertaining history of attempts to achieve more colorful results by overcoming the limitations of 4-color printing. But what of the situation today? What does “Extended Gamut” mean in the present context; what do these solutions look like? EG systems now range from simple arrangements consisting of nothing more than Photoshop and a multichannel output profile to entire integrated workflows with proofing and elaborate options for spot color handling. But for all the recent attention paid to the subject, there is still a lack of industry consensus on just what a constitutes a proper EG separation, and any effort to make sense of the subject typically faces a mixture of conflicting proprietary claims, incomplete studies, and persistent misconceptions. It is still the Wild West. Perhaps by considering how these systems came to exist we might better understand what they actually do and how well they fulfill their purpose.
Extended-gamut today: a dual heritage
We can simplify the origins of all EG systems to two distinct lineages, each corresponding to a different need: Systems that convert images and those that convert spot colors. Combining and reconciling these two functions is the key challenge faced by designers of EG systems today.
Spot color conversion systems: a tyranny of rules
As we read earlier, several decades ago some clever individuals saw that one might reproduce a wide range of spot colors as equivalent process builds using CMYK with the addition of secondary inks such as a red, orange, green, blue, or violet. This was in the days before digital color management, so lookup tables were laboriously built from trial press runs and served as guides for converting colors, object by object. These systems mostly used AM screening, so immediately the question of screen angles had to be addressed. The usual scheme of 0, 15, 45, and 75 degrees was seen as imperfect because it forced a choice of either creating additional intermediate screen angles, increasing the risk of moirĂ©, or sharing screening angles among complimentary colors such as cyan and red/orange and magenta and green, an unpopular choice as it raised the risk of color shifts caused by misregistration on press—a reasonable concern when printing vector objects, with their well-defined boundaries. A near-consensus converged around two simple rules for multicolor separations:
1. No color shall be converted to more than 4 process colors. Three is even better.
2. Complimentary overprints, such as cyan-red/orange and magenta-green, must be prevented. Who needs them, anyway?
These rules continue to be applied in a majority of EG systems, even those updated with automatic color-managed conversions. Spot color tints and overprints are handled by arithmetic interpolation or other simple means. The intricacies, and limitations, of these schemes will be discussed in the next blog. For the remainder of this one, we’ll focus on image conversions.
Images conversion systems: the rise of multichannel ICC profiles
While those brave pioneers were building their lookup tables, deft prepress workers were enhancing CMYK images with “touch” or “bump” plates of stronger colors. This practice can be seen as the true progenitor of multichannel extended-gamut systems.
With rare exceptions, modern image conversion methods rely on ICC color management. For generating multicolor (CMYK+N) press separations a basic system includes two profiles, one for the source color apace (often RGB) and one for the multicolor destination space, a CMM (Color Matching Method), AKA a “color engine,” and an application such as a RIP, color server, or other color-managed program such as Photoshop to interpret the source pixels and build the new image.
The profiles contain lookup tables that translate color appearance values (XYZ or Lab, AKA the PCS, or Profile Connection Space) to device values and vice versa. The CMM draws smooth curves through the LUT points and interpolates all intermediate values. PCS to device (or B2A) tables may contain a good deal of “secret sauce” for enhancing printability, saving ink, increasing the amount of black in neutrals, etc.; separate tables are built for different rendering intents or gamut mapping strategies. On top of this, device-link profiles may be employed to bypass the PCS conversion and apply even more rules, e.g., to exempt certain colors from the conversion. The key points to know about multicolor ICC systems are:
They are automatic, fast, and precise.
They are able to juggle multiple objectives, the two most important being accuracy and smoothness of output color, even up to 7 or more output channels.
Any combination of output channels may be used to fulfill objectives.
Results are conditioned by the quality and completeness of underlying measurement data.
Two systems, two outcomes
Image conversion is still an important function of EG, and systems do perform differently depending on their underlying logic. To illustrate this we can look at the results from converting an RGB test image with two very different solutions, one developed primarily for simulation of spot colors—you might say an heir to those clever lookup tables—and the other a typical representative of ICC color-management, the current default method for converting images. The test image contains a smorgasbord of truly devilish color conversion challenges: delicate flesh tones, textures and smooth gradations in deep, saturated colors, and full-tone black and white images. Any serious defect in a color conversion system is unlikely to escape detection here.
Details: Some gamut compression will be required convert these images to these smaller multicolor output spaces: In the first example perceptual rendering intent was used; in the second, relative colorimetric with black point compensation was chosen as the best available option with that system. Black generation (GCR) was adjusted to be as similar as possible in both cases.
System A. This is a conventional ICC-based system, available as a standalone profiling application (to be used in ICC-compliant workflows) and optional color server, which was employed here for the conversion. The underlying measurement data of the profile (for coated packaging board) is plotted in Lab space below. It shows a good balance of shadow, mid-tone, and highlight samples. A modest number of complementary color pairs (C-O, M-G) and extra-color overprints (O-G, V-G, OV, OVG) are present.
The results, shown below, are good. Details and tonal separations are preserved in the deepest and most saturated colors; gray balance is excellent, and no contouring, banding, or posterization is evident. Gradations are smooth.
The OGV separation view below shows a long scale for the OGV channels; they extend deep into colors that are printable with CMYK alone (flesh tones, underside of sunflower).
The graph below, derived from the lower-right image of the jack-o-lantern plant, shows how a range of colors from red-orange to dull green is composed. The extensive interleaving of channels, including small amounts of complementary colors, is a very probable contributor to the visual smoothness of the color transitions.
This system would be a good choice for demanding image reproduction work.
System B. Here we have a non-ICC system sold as an option for a popular workflow suite. It is a bit of a hybrid, its profiling scheme showing echoes of earlier simple spot-color lookup systems: Complimentary colors (C-O, M-G), extra colors (O.G.V) do not overprint, and no build exceeds 4 total colors. Otherwise, its profiles contain an abundance of tint and overprint data, as seen in this Lab plot:
The profile structure is unique, consisting of 4 4-color charts, thus simplifying its design. As we’ll see, simpler is not necessarily better, as when approaching multidimensional problems like image transforms in 7-color space.
The converted test image below predictably shows some differences with Sample A. Color transitions are more abrupt, as shown clearly in the patch chart at the top of the form.
The OGV separation view shows very limited replacement of in-gamut CMY by OGV inks, though this reportedly may be adjusted to some degree in the software. Gradations in these channels are noticeably less differentiated than in System A. The overall appearance of the n-channel separations is more like that of old-fashioned touch colors than fully functioning process channels. The red-green transition contains much less channel interleaving than in Sample A.
Two image comparisons will suffice to show some implications for these different separation schemes. In the lower area of the still life image below we see the dramatic impact of the compacted tonal scale of the violet channel in System B (right). Deep blues are hollowed out and posterized. Faithful rendering of dark, saturated colors is a critical attribute of any good color reproduction system, and this example is decidedly sub-par. System A (left) shows normal results.
The next image detail is a good test for reproduction of subtle color transitions. As seen in the System A image (left), the jack-o-lantern displays a nicely nuanced transition from orange to yellow-green in which a multitude of slightly varying flecks of yellow-green and yellow-orange can be seen. The System B image (right) looks comparatively crude, with a relatively flat, featureless orange abruptly breaking to an undifferentiated yellow-green. (This may be difficult to see in this low-resolution image.) Such defects are not correctable through image retouching, as the separations simply lack the necessary supporting tonal information.
This image also shows an interesting feature of System A. In the transitional color regions we see the presence of the complementary orange-cyan combination:
These colors, as well as green and magenta, are of course mutually cancelling and therefore commonly regarded as unnecessary in a press separation, a belief so widely accepted that it might be regarded as one of the tenets of extended-gamut printing. As noted earlier, such combinations are excluded from the separation in System B. However, closer study reveals a useful role for these “interstitial” colors in smoothing transitions.
A cautionary note on profile accuracy
An otherwise capable multicolor system may be compromised by the sort of measurement data underlying the profile being used. Here is a Lab plot of a typical 7-color measurement set used by a popular ICC profiling application.
Notice the extreme abundance of dark overprints, far in excess of any possible utility, and the skeletal representation of mid-tones and highlights. This gaping void must be interpolated, literally guessed at by the CMM. The resulting converted images are smooth, but intermediate values are largely fictitious. The less linear the device output is the worse the fiction! There is no known workaround with this application.
Conclusions
Modern multicolor extended-gamut systems must capably convert a variety of elements, including images, vector objects, and spot and process colors, singly and in combination. Systems based on older spot-color matching schemes may have a tougher time converting images compared with systems based on ICC multichannel profiling. Nonetheless, these older systems continue to enjoy popularity in package printing, where the dominance of vector designs gives rise to concerns about printability that may compete with the need for high image fidelity. In the next blog we’ll see how these two schools of thought play out in actual practice, with particular focus on strategies and techniques for converting spot colors. If space allows we’ll also touch upon the arcane subject of EG proofing.
Are you having a multicolor crisis of your own? Are battles over your system configuration leaving you black and blue? Please feel free to post comments and questions here, or send them to mike@mspgraphics.com.
About today's blogger
Mike Strickler is the guy I call on the rare occasions when I want to sully my mind with practical concerns about color management. Mike is a specialist in graphics arts and color management. He is an Idealliance G7 Expert, CMP Color Management Master Trainer and member of PIA and IAPHC. Based in the San Francisco Bay and Los Angeles areas. Specialties: Color management, printing, remote proofing, and photography.
Mike is the principal at MSP Graphic Services.
Tuesday, June 26, 2018
Looking for case studies!
Proof of concept has been established on my ColourSPC project. Over 561K color measurements have been compiled of roughly 3,000 production colors from nine different sources, including data from packaging, newspaper, toner-based, and offset printing, as well as photography, and plastics. The analysis demonstrated that when a color process is in control, the new Zc statistic will follow a specific known distribution.
I am moving to the proof of utility phase, where I hope to show that my new techniques can turn color data into information that is useful for color manufacturers.
I am looking for case studies; people with color manufacturing data and a burning question that they would like answered with that data. Contact me if you want to be part of this exciting new research! john@johnthemathguy.com
Example questions
Some examples of questions that can be answered with ColourSPC:
Is this data point an outlier, or just somewhat unusual?
Was this production run under control?
What is the major contributor to color variation?
Did this new piece of equipment or software, or a change in process reduce color variation?
Can this process reliably meet the color tolerance that my customer wants?
References
Statistics
of multi-dimensional data, theory, Nov 14, 2018
Statistics
of multi-dimensional data, example, Nov 21, 2017
Is
my color process going awry?, Feb 14, 2018
Is
my color process all wonky?, Feb 28, 2018
Statistical process control of color, TAGA, March 2018
Applications of ColorSPC, Print Properties Council, March 2018
Wednesday, May 30, 2018
How well do we remember color?
In a previous (and highly entertaining) blog post, I reviewed two studies that tested people on brand color recognition. The studies were not peer-reviewed, Nobel prize winning efforts, but enough effort was put into them for me to find the results suggestive. I don't mean that in a salacious way; I do mean that the experiments suggest that our recognition of brand colors is not as good as we might think it is.
The sight of Starbucks periwinkle makes me thirsty
In today's post, I will take a dive into what peer reviewed Science has to say on the matter, specifically on the topic of how accurately we remember colors.
What I didn't study
There are a lot of stones in this field, and I left a lot of them unturned. Here are some exciting topics that I skipped over...
Color and emotion - This is a big field. It would be cool to really dig into how color effects us emotionally. I would love to separate the wheat from the chaff (valid research from pontification). The paper by Yu et al. Looks like it would be fascinating. But, I didn't really look at it for this blog post.
Color and emotion - This is a big field. It would be cool to really dig into how color effects us emotionally. I would love to separate the wheat from the chaff (valid research from pontification). The paper by Yu et al. Looks like it would be fascinating. But, I didn't really look at it for this blog post.
Color and emotion and brands - Another mondo interesting study would be to look at our emotional reaction to the color of a box of cornflakes. Presumably, there is an ideal corn flake box color that will seduce the unwary buyer into filling the shopping cart. Gosh, I would love to learn all about that. I bet the paper by Rupert et al. would be a great place to start.
Color constancy - There has been all this research around the cool topic of how it is that our perception of the color of something doesn't change all that much when the illumination changes drastically. Like from the yellowish incandescent light in your living room to the blueish light outside. There is a huge difference in the spectra of the light hitting your eye, but we still see white paper as still being white. This is the kinda topic that makes people want to quit their day job and become a PhD candidate! But I'm not going to talk about it. Well... maybe I will mention it in passing.
Many roads diverged in this multicolored wood, and I am sorry that I could not travel them all. I took the one less traveled.
Color memory
Here's what I am thinking: I have a picture in my head about what Starbucks green is. I see it as a darker shade of green that might be just a tiny bit toward the blue end. But, my memory of Starbucks periwinkle is wrong. If you don't believe me, just ask my wife. She is the world's authority on all my shortcomings, and would love to acquaint you with my multifarious imperfections. But, everyone's memory of the official Starbucks green is not quite perfect. How far off are we?
We all know that a banana is yellow, and a school bus is a slightly orange flavor of yellow, and the color of an orange is slightly weaker than true orange, but that carrots are true orange, which is why we call them carrots. These are all memory colors.
I just can't remember the name of this film
We all know that a banana is yellow, and a school bus is a slightly orange flavor of yellow, and the color of an orange is slightly weaker than true orange, but that carrots are true orange, which is why we call them carrots. These are all memory colors.
Carrots should really be called oranges
The idea of memory colors dates back to Ewald Hering in 1878. Loyal readers will remember that Hering has been mentioned in a few of my previous blog posts. Hering is the guy who developed the color opponent theory. This theory says that we can assess any color in terms of three attributes: where it fits between white and black, where it fits between red and green, and where it fits between yellow and blue. This is how color is encoded on its way to the brain, and this idea was baked into CIELAB.
Hering's three attributes of color
Hering made another contribution to color science. He said that our perception of the color of an object is effected by our memory of the color of a prototypical object. Like, if we see a banana that is kinda yellow, but not quite, we will remember it as being yellow. Remember how I said that I was gonna mention color constancy? Now is the time. Hering's theory has been used to explain color constancy. If the yellow of a banana has a slightly unusual shade, then our brain will use that fact to deduce the color of the illumination.
There is a prediction from Hering's theory of color memory that is important for the purpose of this blog. If his theory is true, then our memory will tend to bias toward the quintessential version of that color. We will remember our bananas being yellower than they really are.
David Katz reiterates Hering's theory:
... in the imagination we exaggerate colours of objects whose colours are generally distinguished only in terms of brightness, darkness or hue. If we ask a person to pick out a blue which will match the colour of the eyes of someone he knows very well, he generally selects a blue which is too saturated. If we ask a person to match a brick, he usually chooses a black which is too deep or reds which are too highly saturated. Almost always he selects a colour which is too bright to match a bright object, one which is too dark to match a dark object, and one which is too saturated to match an object which is known to have a distinct hue.
Katz liked Hering's theory on the distortion of colors by our memory
If this theory is true, then our color memory is flawed, so our recollection of Starbuck's green is flawed. The practical message for everyone in the business of making sure that Starbucks has the correct shade of green is that the exact color of the logo doesn't really matter, since we will identify the logo by it's shape, and our brain will translate the color into the correct shade of green.
Wow. Big stuff here.
What does the research say?
Hering was a brilliant guy, and rates up there with Munsell as one of the Fathers of Color Science. But he was largely a theoretical guy. Have experimental results backed up his theories of memory colors?
An early investigation by Adams provided evidence that agreed with Hering, but it was inconclusive.
From our investigations of the perceptions of the five natural objects grass, snow, coal, gold and blood, we may say that Hering and Katz were correct in claiming that the seeing of these objects is ordinarily affected by memory color. Although our investigation failed to give a quantitative measure of a single memory color, it was thus not unfruitful.
Adams (1923)
Five memory colors from Adams' paper
Not unfruitful? I didn't see a single fruit in their list of memory colors! It is likely that the hard results were lacking because of the lack of rigor in this paper. To be honest, the paper reads like a cheap and boring novel with pages and pages of one anecdote after another, and then a short experiment.
Three recent papers (one by Bloj et al., one by Pentz, and the final by Newhall et al.) sought to eliminate this problem of variability of the colors of real objects. Bloj asked subjects to bring along a familiar object. Even when recalling those familiar and well-defined colors from memory, the conclusion of this paper was that "Our results, on average, confirm that objects are remembered as more saturated than they are."
On to Pentz's paper. He taught a color class for several years, and recruited the members of each class for an experiment, eventually testing 283 people. The people in the class were shown a blue piece of plastic and were told that they would need to recollect the color later on. They each had a chance to hold the plaque and could look at it as long as they wanted. Later, they were shown a collection of 24 plaques which included the one they had looked at. "Only thirty percent of participants at plastics coloring seminars were able to correctly identify a color observed only an hour earlier." While the correct plaque was determined by the most participants, the two next most likely guesses had a color difference of about 10 ΔE from the correct plaque.
(How big is 10 ΔE, you may ask? Imagine a color match that is as big as you might consider acceptable for production. Then multiply that by 2 or 3.)
Newhall et al. looked at our short term memory of colors in order to eliminate the ambiguity for familiar objects. The subject was shown a color for 5 seconds, given a 5 second break, and then asked to adjust knobs to recreate that color. Here is one of the conclusions from the paper:
Significantly more purity and somewhat more luminance were required to complete the color matches by memory than were necessary for the simultaneous matches. This principal result was confirmed by the results of three supplementary experiments.
Newhall, Burnham, and Clark (1957)
Bartleson provided a more rigorous test of memory colors, using colors of ten familiar objects: red brick, green grass, dry grass, blue sky, flesh, tanned flesh, broad-leafed summer foliage, evergreen trees, inland soil, beach sand. Here is their assessment:
Each memory color tended to be more characteristic of the dominant chromatic attribute of the object in question; grass was more green, bricks more red, etc. In most cases, saturation and lightness increased in memory.
There is evidence of increased saturation in the memory colors. In most cases there are hue shifts with memory in the direction of what is probably the most impressive chromatic attribute of the object in question.
There is evidence of increased saturation in the memory colors. In most cases there are hue shifts with memory in the direction of what is probably the most impressive chromatic attribute of the object in question.
Bartleson (1960)
A bit more recently, Siple et al. did a similar test with six foods (carrot, corn, lettuce, lime, orange, and peanut), and agreed with the theme that we remember colors as being more vivid. Note that their test was the first that was literally fruitful, since they included oranges and limes.
Results indicated that, for hue and brightness, memory and preference were quite accurate for the objects tested; however, all subjects remembered and also preferred all items to be more highly saturated.
Siple and Springer, 1983
One could argue that trying to recall the color of grass is a bit problematic. After all, the color of grass varies with species. Is it Kentucky bluegrass? Fescue? Rye grass? Easter grass? The color also varies with the plenitude of rain, nutrients, and sun. And where my doggies have visited.
The grass is always greener in our memory
Results indicated that, for hue and brightness, memory and preference were quite accurate for the objects tested; however, all subjects remembered and also preferred all items to be more highly saturated.
Siple and Springer, 1983
Three recent papers (one by Bloj et al., one by Pentz, and the final by Newhall et al.) sought to eliminate this problem of variability of the colors of real objects. Bloj asked subjects to bring along a familiar object. Even when recalling those familiar and well-defined colors from memory, the conclusion of this paper was that "Our results, on average, confirm that objects are remembered as more saturated than they are."
Somehow Johnnie graduated from Kindergarten despite his sub-par drawing skills
On to Pentz's paper. He taught a color class for several years, and recruited the members of each class for an experiment, eventually testing 283 people. The people in the class were shown a blue piece of plastic and were told that they would need to recollect the color later on. They each had a chance to hold the plaque and could look at it as long as they wanted. Later, they were shown a collection of 24 plaques which included the one they had looked at. "Only thirty percent of participants at plastics coloring seminars were able to correctly identify a color observed only an hour earlier." While the correct plaque was determined by the most participants, the two next most likely guesses had a color difference of about 10 ΔE from the correct plaque.
(How big is 10 ΔE, you may ask? Imagine a color match that is as big as you might consider acceptable for production. Then multiply that by 2 or 3.)
Newhall et al. looked at our short term memory of colors in order to eliminate the ambiguity for familiar objects. The subject was shown a color for 5 seconds, given a 5 second break, and then asked to adjust knobs to recreate that color. Here is one of the conclusions from the paper:
Significantly more purity and somewhat more luminance were required to complete the color matches by memory than were necessary for the simultaneous matches. This principal result was confirmed by the results of three supplementary experiments.
Newhall, Burnham, and Clark (1957)
Newhall et al. found that we remember colors as being higher in chroma (by 1.7 steps in Munsell chroma) and somewhat lighter. They also found no consistent change in hue between what we remember and what we see.
The typical migration pattern of a color trapped in a brain
How big is a step in chroma of 1.7? The data is all in the paper. I could type it all into a spreadsheet, convert it into CIELAB, and then compute color differences. But I could just be lazy and wave my hands. Fully saturated colors go up to maybe 15 in Munsell chroma, and maybe 100 in CIELAB C*. A step of 1.7 in chroma is roughly 10 ΔE. I dunno. I think this is kinda big for a 5 second delay.
One common theme from these experiments is that colors are remembered more vividly than they actually are. Whether or not colors are lighter in our memory and whether there is a systematic error in the hue are both up for debate.
Colligation
The ancient Greek, Ptolemy, developed a set of equations that could be used to predict the positions of the planets at any time. The equations were based on a lot of wrong assumptions, like "the Earth is in the center, and all the rest of the celestial bodies move in circles that revolve around other circles". The model worked, at least to an extent.
A millennium or so later, Copernicus decided that the Sun belonged at the center. Then Kepler came along and decided that ellipses made the whole thing simpler than the circles in circles thing, and then Newton provided the big colligation. The inverse square law of gravity was the grand unifying theory that explained the whole enchilada. If the pull of gravity goes in inverse proportion to the square of the distance between the objects, then planets will travel in Kepler's ellipses. Eureka!
By the way, colligation means "to subsume (isolated facts) under a general concept". I really love that word. It explains the essence of what I think it is to do Science: to find simple theories that explain lots of data.
We are ready for a colligation of our understanding of color memory. We have all this data from these studies. We have a generalization of how saturation changes when it gets implanted into our memory. It's a bit fuzzy what happens with hue and lightness, since the data doesn't always agree. We need an explanation that can tie it all together and explain some of the anomalies.
In this case, the colligation was fairly recent, by Bae et al. in 2015. The idea is pretty simple. We have a limited number of folders in the filing cabinet in our head. Although we can distinguish perhaps millions of colors, there are eleven basic folders where we store colors, at least in the English speaking world. The folders are labeled white, black, gray, red, orange, yellow, green, blue, pink, brown, and purple. This was the result from Berlin and Kay, and also in the groundbreaking experiment that I never got around to publishing.
Here is the grand and glorious theory of color memory distortion.....
When we want to store the color of an object in our brain, the first step is to categorize it into one of perhaps eleven archetypal colors. From there, presumably, we may make modifications to distinguish from the archetypal color (yellowish green, or dark red), and the modifications get stored along with the general category. Later when we retrieve that color from our memory, the archetypal color gets weighted a bit more than the modifier.
Evidence from Bae
Bae et al. had the participants try to remember 180 colors, equally spaced in hue, all with L* = 70 (fairly light) and C* = 38 (somewhat saturated). They saw the color for 100 msec, the color was removed for 900 ms, and then they had a chance to select the color from a ring.
The results of the paper are summarized in my drawing below. There are seven regions. Within any of the regions, for example, the blue one, people will tend to distort the hue toward the solid line which represents the archetypal example of that hue. (Four of the eleven basic colors were left out. White, black, and gray don't have a place on the hue circle. And since their L* was fairly high, they missed out on brown. They almost missed red.)
Why eleven?
Bae's research suggests that the eleven basic color names are the appropriate number. Or rather, it does not suggest that there are colors beyond the seven which qualify to be archetypal colors. But Bae's experiment, awesome as it was, only looked at 180 colors - all of which had the same L* and C*. There is quite a bit of uncharted color space.
Based on my personal experience, I would like to think that I have more than just eleven archetypal colors. I mean, I see tan, coral, olive green, and plum as distinct colors in their own right.
In some languages, such as Russian, Japanese, and Italian, there is a separate word for light blue which stands on its own as a distinct color. So, maybe there are twelve archetypal colors? Dimitris Mylonas (Mylonas and MacDonald, 2015) suggested that lilac and turquoise also belong on the list. In two other papers, he has named a much larger collection, including cream, lime, olive, salmon, mustard, peach, tan, and coral.
So, I don't think we can say at this time that there are exactly eleven colors that serve as archetypal colors in our memory. There could be more. It also seems quite plausible (to me) that the number is different for different people. I would think that people who deal with colors all the time (like artists, graphic designers, fashion designers, interior decorators, and the spouses of color scientists) might have developed a wider collection of focal point colors. On the other hand, it could be that the relatively small collection of focal point colors are a result of something hardwired in the brain.
Here's another interesting thought. We know that some people have perfect pitch, an uncanny knack to identify musical notes. All of these studies looked at people's color memory in the aggregate. Perhaps there were a few individuals whose superpower is to have perfect hue? I have heard more than one person make that claim. Of course, one person who made that claim also told me that he was raised from infancy by a troop of iguanas in a volcanic crater. He probably learned it from them.
All interesting stuff for further research!
When we want to store the color of an object in our brain, the first step is to categorize it into one of perhaps eleven archetypal colors. From there, presumably, we may make modifications to distinguish from the archetypal color (yellowish green, or dark red), and the modifications get stored along with the general category. Later when we retrieve that color from our memory, the archetypal color gets weighted a bit more than the modifier.
Evidence from Bae
Bae et al. had the participants try to remember 180 colors, equally spaced in hue, all with L* = 70 (fairly light) and C* = 38 (somewhat saturated). They saw the color for 100 msec, the color was removed for 900 ms, and then they had a chance to select the color from a ring.
The results of the paper are summarized in my drawing below. There are seven regions. Within any of the regions, for example, the blue one, people will tend to distort the hue toward the solid line which represents the archetypal example of that hue. (Four of the eleven basic colors were left out. White, black, and gray don't have a place on the hue circle. And since their L* was fairly high, they missed out on brown. They almost missed red.)
Hieroglyph found in a Mayan tomb, hitherto-for undecipherable
This explains why the hue of a color sometimes shifted in the experiments, and sometimes not. But the color memory experiments seem to all agree on one thing. Our memory of a color is generally more saturated than the actual color. How does the eleven-folder theory of color memory explain this?
Here is a quote from Heider that can explain this:
It was quite clear, without further analysis, that the most saturated colors were the best examples of basic color names both for English speakers and for speakers of the other 10 languages represented.
When we think red, we don't think some wimpy-butt red. We think fire-engine-lipstick-Corvette-candy-apple-OMG-I'm-bleeding red. It only makes sense that most of the colors that were tested in the experiments cited above would not be the most saturated colors imaginable. Hence, our memory would tend to distort most of the colors in the experiments toward the extreme of saturation.
There are some archetypal colors that don't fit Heider's hypothesis, namely brown, gray, and pink. I would take a wild guess that these are the exceptions to the rule in the previously described experiments.
Bae's research suggests that the eleven basic color names are the appropriate number. Or rather, it does not suggest that there are colors beyond the seven which qualify to be archetypal colors. But Bae's experiment, awesome as it was, only looked at 180 colors - all of which had the same L* and C*. There is quite a bit of uncharted color space.
Based on my personal experience, I would like to think that I have more than just eleven archetypal colors. I mean, I see tan, coral, olive green, and plum as distinct colors in their own right.
My candidates for induction into the Hall of Archetypal Colors
In some languages, such as Russian, Japanese, and Italian, there is a separate word for light blue which stands on its own as a distinct color. So, maybe there are twelve archetypal colors? Dimitris Mylonas (Mylonas and MacDonald, 2015) suggested that lilac and turquoise also belong on the list. In two other papers, he has named a much larger collection, including cream, lime, olive, salmon, mustard, peach, tan, and coral.
So, I don't think we can say at this time that there are exactly eleven colors that serve as archetypal colors in our memory. There could be more. It also seems quite plausible (to me) that the number is different for different people. I would think that people who deal with colors all the time (like artists, graphic designers, fashion designers, interior decorators, and the spouses of color scientists) might have developed a wider collection of focal point colors. On the other hand, it could be that the relatively small collection of focal point colors are a result of something hardwired in the brain.
Here's another interesting thought. We know that some people have perfect pitch, an uncanny knack to identify musical notes. All of these studies looked at people's color memory in the aggregate. Perhaps there were a few individuals whose superpower is to have perfect hue? I have heard more than one person make that claim. Of course, one person who made that claim also told me that he was raised from infancy by a troop of iguanas in a volcanic crater. He probably learned it from them.
All interesting stuff for further research!
Non-References
Burnham, Robert W., and Joyce Clark, A Color Memory Test, Journal of the Optical Society of America, Vol 44, No 8, Aug 1954
Rupert, Andrew Hurley, Rachel Randall, Liam O'Hara, Charles Tonkin, Julie C. Rice, Color harmonies in packaging, Color Research & Application, Volume 42, Issue 1, 28 March 2016
Yu, Luwen, Stephen Westland, Zhenhong Li, Qianqian Pan, Meong Jin Shin, Seahwa Won, The role of individual colour preferences in consumer purchase decisions, Color Research & Application, Volume 43, Issue 2, 10 October 2017
References
Adams, Grace Kinckle, An Experimental Study of Memory Color and Related Phenomena, The American Journal of Psychology, Vol. 34, No. 3 (Jul., 1923), pp. 359-407
Bae, Gi-Yuel, Maria Olkkonen, Sarah R. Allred, and Jonathan I. Flombaum, Why Some Colors Appear More Memorable Than Others: A Model Combining Categories and Particulars in Color Working Memory, Journal of Experimental Psychology: General, 2015, Vol. 144, No. 4, 744–763
Bae, Gi-Yuel, Maria Olkkonen, Sarah R. Allred, and Jonathan I. Flombaum, Why Some Colors Appear More Memorable Than Others: A Model Combining Categories and Particulars in Color Working Memory, Journal of Experimental Psychology: General, 2015, Vol. 144, No. 4, 744–763
Bartleson, C. J., Memory Colors of Familiar Objects, Journal of the Optical Society of America, Vol 50, No 1, Jan 1960
Berlin, B., and P. Kay, Basic color terms: their universality and evolution (Stanford, Calif.: Center for the Study of Language and Information 1969).
Katz, David, The World of Colour, Kegan, Paul, Trench, Tubner, 1935, p. 164
Heider, Eleanor Rosch, Universals in color naming and memory, Journal of Experimental Psychology, 1972, Vol. 93, No. 1, 10-20
Hering, Ewald, Outlines of a theory of light sense, GrundzĂ¼ge der Lehre vom Lichtsinn 1905, translated 1964, Harvard University Press
Mylonas, Dimitris and Lindsay MacDonald, Online Colour Naming Experiment Using Munsell Samples, European Conference on Colour in Graphics, Imaging, and Vision - CGIV, June 2010
Mylonas, Dimitris, Mathew Pruver, Mehrnoosh Sadrzadeh, Lindsay MacDonald, and Lewis Griffin, The Use of English Colour Terms in Big Data, May 2015, AIC Midterm 2015
Mylonas, Dimitris and Lindsay MacDonald, Augmenting Basic Colour Terms in English, Color Research and Application, Volume41, Issue 1, February 2016
Berlin, B., and P. Kay, Basic color terms: their universality and evolution (Stanford, Calif.: Center for the Study of Language and Information 1969).
Katz, David, The World of Colour, Kegan, Paul, Trench, Tubner, 1935, p. 164
Heider, Eleanor Rosch, Universals in color naming and memory, Journal of Experimental Psychology, 1972, Vol. 93, No. 1, 10-20
Hering, Ewald, Outlines of a theory of light sense, GrundzĂ¼ge der Lehre vom Lichtsinn 1905, translated 1964, Harvard University Press
Mylonas, Dimitris and Lindsay MacDonald, Online Colour Naming Experiment Using Munsell Samples, European Conference on Colour in Graphics, Imaging, and Vision - CGIV, June 2010
Mylonas, Dimitris, Mathew Pruver, Mehrnoosh Sadrzadeh, Lindsay MacDonald, and Lewis Griffin, The Use of English Colour Terms in Big Data, May 2015, AIC Midterm 2015
Mylonas, Dimitris and Lindsay MacDonald, Augmenting Basic Colour Terms in English, Color Research and Application, Volume41, Issue 1, February 2016
Newhall, S, M., R. W. Burnham, and Joyce R. Clark, Comparison of Successive with Simultaneous Color Matching, JOSA 47, No. 1, Jan 1957
Pentz, Anthony J., Does color memory exist?, SPE/ANTEC 1999 Proceedings (Society of Plastics Engineers Annual Technical Conference and Exhibit)
Siple, Patricia, and Robert Springer, Memory and preference for the colors of objects, Perception & Psychophysics, 1983,34 (4), 363-370
Thursday, May 17, 2018
Do you remember a logo?
I stumbled across a quote the other day that I found interesting. This was on the Coca-Cola website:
"There is no Pantone color for Coca-Cola red, but when you see it, you know it."
Ah! To be tanning under the Coke Red Sun!
This sounds like one of those factoids that everyone knows is true, so nobody would be crazy enough to actually test it. Well, guess what? I know a few crazy people. In fact, one of my best friends, Eddy Hagen, has recently tested this very thing with an online test: how well can you pick out Coke red?
(As an aside, here is the process for joining the John the Math Guy's Best Friend Club: Connect with me on social media. Contact me somehow or other with a message that does not contain the phrase "John the Math Guy is a doofus." Then you're in. If you just want to get on my email list, then send an email to john@johnthemathguy.com to subscribe.)
There are two tests in Eddy's blog. In the first test, Eddy tests your short-term color memory . You are shown a color, and then asked to pick it out of a line-up later. That one is kind of a warm-up to the real test. In the second test, he shows you a bunch of colors and asks you to pick out Coke red.
Do people know Coke red when they see it, as the Coca-Cole website suggests? He shares the results in another blog post. I don't wanna give anything away, but the title of this post is You can’t correctly remember an iconic color, not even Coca-Cola red.
Which one makes you thirsty?
Who is right??!?!? Let's get to the bottom of this!
Brand color is important
Brand colors are important, especially if you have a brand to sell. Here is what Axel Kling (Print Quality Assurance Manager for Coke) has to say about the importance of brand colors:
In today’s marketplace of unlimited beverage choices, a brand’s first point of contact is most likely to be at the point of purchase. And how well your product stands out on shelf could determine whether it’s put in the shopping cart or left behind.
I know most of my readers have private chefs who do their grocery shopping, but imagine if you will, being in the snack aisle of a grocery. You are trying to find your favorite bran cereal with raisins. Just reading that line, I'm gonna guess that you're thinking "purple". Am I right??! Of course, the image below wasn't any clue.
The bran owners of the various raisin brands have trained their cereal boxes to be distinctive colors so that they can jump off the shelf into your shopping cart. And lets, face it. Nothing says "raisin bran demographic" quite like the color purple.
This is an aside, but how can Kellog's and Post and Trader Joe's and Total and John the Math Guy Breakfast Foods all use the name Raisin Bran? Interesting trademark factoid: The Skinner Manufacturing Company was the first to sell raisin bran, back in 1926. It trademarked the name, but in 1944, the Supreme Court rescinded the trademark, saying that you can't trademark a simple description of a product.
Speaking of trademarks, the color purple, and brand colors ... In 2004 Cadbury applied for a British trademark for the color purple (Pantone 2685C) "applied to the whole visible surface, or being the predominant colour applied to the whole visible surface, of the packaging of the goods." Nestle objected, and their application was denied. It seems you kinda have to have a mark, if you want to have it trademarked. But, this trademark application in 2004 was a revision of an earlier trademark from 1995, which is still in force, at least until Nestle contests that trademark.
This is in the UK. I apologize in advance to my British friends and enemies, but I'm not all that excited about British law. I mean, back in 1492, we fought the Spanish-American war to get away from having to follow your laws about tacks in our tea. What about US trademark law and colors?
I read up a bit on Wikipedia about color trademarks. In the US, you can trademark a color so long as it serves no other purpose other than to distinguish your product. So, Johnson & Johnson can trademark the name Band Aid, but not the color, since that serves as camouflage on certain people's skin.
There are a number of colors that are trademarked in the US, as shown in the image below. I compiled these from the Wikipedia article and the Business Insider article Can You Identify These 12 Brands By Their Trademarked Colors Alone?
It's not just about being able to find your favorite cereal
In today’s marketplace of unlimited beverage choices, a brand’s first point of contact is most likely to be at the point of purchase. And how well your product stands out on shelf could determine whether it’s put in the shopping cart or left behind.
I know most of my readers have private chefs who do their grocery shopping, but imagine if you will, being in the snack aisle of a grocery. You are trying to find your favorite bran cereal with raisins. Just reading that line, I'm gonna guess that you're thinking "purple". Am I right??! Of course, the image below wasn't any clue.
When I am old, I shall eat cereal out of purple boxes
The bran owners of the various raisin brands have trained their cereal boxes to be distinctive colors so that they can jump off the shelf into your shopping cart. And lets, face it. Nothing says "raisin bran demographic" quite like the color purple.
This is an aside, but how can Kellog's and Post and Trader Joe's and Total and John the Math Guy Breakfast Foods all use the name Raisin Bran? Interesting trademark factoid: The Skinner Manufacturing Company was the first to sell raisin bran, back in 1926. It trademarked the name, but in 1944, the Supreme Court rescinded the trademark, saying that you can't trademark a simple description of a product.
Speaking of trademarks, the color purple, and brand colors ... In 2004 Cadbury applied for a British trademark for the color purple (Pantone 2685C) "applied to the whole visible surface, or being the predominant colour applied to the whole visible surface, of the packaging of the goods." Nestle objected, and their application was denied. It seems you kinda have to have a mark, if you want to have it trademarked. But, this trademark application in 2004 was a revision of an earlier trademark from 1995, which is still in force, at least until Nestle contests that trademark.
Imagine my surprise when I found out that the chocolate wasn't purple!
This is in the UK. I apologize in advance to my British friends and enemies, but I'm not all that excited about British law. I mean, back in 1492, we fought the Spanish-American war to get away from having to follow your laws about tacks in our tea. What about US trademark law and colors?
I read up a bit on Wikipedia about color trademarks. In the US, you can trademark a color so long as it serves no other purpose other than to distinguish your product. So, Johnson & Johnson can trademark the name Band Aid, but not the color, since that serves as camouflage on certain people's skin.
There are a number of colors that are trademarked in the US, as shown in the image below. I compiled these from the Wikipedia article and the Business Insider article Can You Identify These 12 Brands By Their Trademarked Colors Alone?
I am gonna conclude that at the very least, brand owners think that brand colors are important.
It's not just about being able to find your favorite cereal
Color is about brand recognition, It helps you find a specific product within a dazzling array of colors. But the prevailing wisdom is that it also communicates something about a product. Red universally means romance or hookers, except when it's used on a fire truck or a stop sign. And of course, it doesn't mean romance if you are in China, where red signifies joy and luck. Or on one of my earlier blog posts where I decided it just signifies excitement, which explains why double-decker buses are red. But trust me. The meaning of a color is universal and unambiguous.
I have heard several presentations at conferences where the speaker says something like "color accounts for 86.3% of our buying decisions". As a math guy, I know that 95.4% of all statistics are made up, so, is there any definitive research behind the importance of brand color? Or is this just one of those statistics that gets quoted enough so that it becomes established fact?
Here is a quote from Daivata Patil that sounds authoritative:
Color is ubiquitous and is a source of information. People make up their minds within 90 seconds of their initial interactions with either people or products. About 62‐90 percent of the assessment is based on colors alone.
Authoritative, with numbers and everything. But the article does not describe how these numbers were determined or even give a reference to where they came from. Hmmm.... urban legend?
Here is a similar quote from Axel's presentation. Remember Axel? The color guy with Coke? He attributes this quote to Jill Morton's Color Matters website. Both attribute it to Loyola University.
Color increases brand recognition by up to 80%.
I googled this quote to try to find a link to the actual study. Note that I put quotes around the words so that Google knew that I was looking for those exact words in that order. Goggle told me there were "About 2,170,000 results"! I admit to not reading through them all. I looked at the first ten hits, trying to find the title or author of the study, or maybe a link. All of them mention Loyola, and several of the web pages reference Jill Morton. None of them give any more information about the study.
Time for an infamous John the Math Guy tirade. This is not Science. I'm not saying that I have reason to doubt the statement, or that the various places that provide this quote are required to track down and report the original source. It's just that, for me, I would like to assess the strength of the argument. Was this an undergrad student who made up the numbers the night before the term paper was due? A professor who assembled twenty students for a little test? Or was this a master's thesis with hundreds of volunteers following a rigid experimental protocol?
Gregory Ciotti expresses my concern a bit more emphatically than wishy-washy me:
Most of today's conversations on colors and persuasion consist of hunches, anecdotal evidence and advertisers blowing smoke about "colors and the mind."
Getting back to the topic
John spent the better part of an afternoon looking for his cereal
I have heard several presentations at conferences where the speaker says something like "color accounts for 86.3% of our buying decisions". As a math guy, I know that 95.4% of all statistics are made up, so, is there any definitive research behind the importance of brand color? Or is this just one of those statistics that gets quoted enough so that it becomes established fact?
Here is a quote from Daivata Patil that sounds authoritative:
Color is ubiquitous and is a source of information. People make up their minds within 90 seconds of their initial interactions with either people or products. About 62‐90 percent of the assessment is based on colors alone.
Authoritative, with numbers and everything. But the article does not describe how these numbers were determined or even give a reference to where they came from. Hmmm.... urban legend?
Here is a similar quote from Axel's presentation. Remember Axel? The color guy with Coke? He attributes this quote to Jill Morton's Color Matters website. Both attribute it to Loyola University.
Color increases brand recognition by up to 80%.
I googled this quote to try to find a link to the actual study. Note that I put quotes around the words so that Google knew that I was looking for those exact words in that order. Goggle told me there were "About 2,170,000 results"! I admit to not reading through them all. I looked at the first ten hits, trying to find the title or author of the study, or maybe a link. All of them mention Loyola, and several of the web pages reference Jill Morton. None of them give any more information about the study.
Gregory Ciotti expresses my concern a bit more emphatically than wishy-washy me:
Most of today's conversations on colors and persuasion consist of hunches, anecdotal evidence and advertisers blowing smoke about "colors and the mind."
Getting back to the topic
Let me take a minute to try to remember where I was going with all of this. Oh yeah. Eddy Hagen's experiment about Coke red recognition.
Eddy's online experiment carefully explains the methodology and the results. It's Science, but I'm not gonna claim that Eddy's online experiment is good solid Science, and I don't think Eddy would either. He acknowledges that not all monitors are calibrated, and surveys where the participants are self-selected are a bit less rigorous that random selection. It could be that zealous PepsiCo employees deliberately failed the test to discredit their competitor. Or it could be that some of the individuals clicked at random just cuz it was late at night and they were waiting for the pizza guy to arrive.
Signs.com did another test of people's ability to recall brand logos. They brought in 156 people, and had them draw the logos of ten well-known companies from memory. This involved recalling not only color, but the shape and text of the logo.
They have some stats on various aspects of the logos, but I did my own counting. I looking only at whether they got all the right colors, without adding extraneous ones. The results below are not all that fabulous, especially for multi-colored logos.
I will point out that I was rather lenient about allowing different shades of the correct color. I allowed an orange flavored yellow to count as a yellow, or for Ikea blue to be too light or too dark. The image below shows the variation in color for Satyrbucks, which uses only green in the logo.
At the far left, you see all 156 logos as drawn by the participants in the survey. (You can see a full sized version of this on Signs.com website.) In the middle drawing, I pulled all of the green pixels from each drawing, and averaged them together to show the green that the participant chose. At the far right, I show the 21 contestants that came within 10 DE2000 of the true Starbucks green. For reference, a common tolerance for commercial printing a color is 3.0 DE2000. Only two people out of the 156 participants were able to create a color from memory that would have been deemed acceptable printing of that logo.
Caveats...
I have made the assumption that there was an unbroken chain of proper color management throughout this process. If I had to put money on that, I would say that I would prefer to not put money on that. I don't say that to disparage Signs.com at all. I just know that the bar for rigor in Science is pretty high. But, looking at the middle image above... I rather doubt that any deficiencies in the rigor of this test could have caused that much variation in color.
Another caveat is in the interpretation of the results. This is a test of the participant's ability to recall the proper color from memory (as in Eddy's Coke red test), but also a test of the participant's ability to reproduce that color using the software provided. So, the logo drawing test is harder than the task of trying to find your favorite raisin bran.
Pondering
Eddy provided me with an interesting anecdote: "To put that unique Coke red in perspective: in the LinkedIn ‘Printing Production Professionals’ one of the printers that works for Coca-Cola shared that in the X-mas edition, the Coke red is slightly darker… (which I checked in my collection of Coke cans and it is correct) So if color is soooooo important, how does this different Coke red impact sales?"
I'm still kinda pondering why Eddy has a Coke can collection... but these two experiments beg the question about how precisely a brand color needs to be defined. Both experiments are well above the level of urban legend expressed by the statement "Color increases brand recognition by up to 80%". But neither experiment quite fulfills the high bar of rigor required to be accepted as peer-reviewed Science with a capital S. I don't expect to see either in the next edition of Color Research and Application.
But, the two experiments are suggestive, and that suggestion is a contradiction between the brand owner's expectations of what is needed and the psycho-physics of the color that we see.
In the next installment in this series, I will take a closer look at the Science that has been done, especially the Science having to do with our memory of colors. If you want a bit of a foretaste, look through the references below. I am going to pretend to have digested them in the next blog post.
References
Eddy's online experiment carefully explains the methodology and the results. It's Science, but I'm not gonna claim that Eddy's online experiment is good solid Science, and I don't think Eddy would either. He acknowledges that not all monitors are calibrated, and surveys where the participants are self-selected are a bit less rigorous that random selection. It could be that zealous PepsiCo employees deliberately failed the test to discredit their competitor. Or it could be that some of the individuals clicked at random just cuz it was late at night and they were waiting for the pizza guy to arrive.
Signs.com did another test of people's ability to recall brand logos. They brought in 156 people, and had them draw the logos of ten well-known companies from memory. This involved recalling not only color, but the shape and text of the logo.
Can you draw these from memory?
They have some stats on various aspects of the logos, but I did my own counting. I looking only at whether they got all the right colors, without adding extraneous ones. The results below are not all that fabulous, especially for multi-colored logos.
Company
|
Correct
|
Colors
|
7-Eleven
|
14%
|
Green, Orange, Red
|
Burger King
|
20%
|
Blue, Orange, Red
|
Foot Locker
|
38%
|
Black, Red
|
Walmart
|
49%
|
Blue, Yellow
|
Dominos
|
53%
|
Blue, Red
|
Starbucks
|
53%
|
Green
|
Ikea
|
56%
|
Blue, Yellow
|
Apple
|
72%
|
Black
|
Target
|
84%
|
Red
|
Adidas
|
92%
|
Black
|
I will point out that I was rather lenient about allowing different shades of the correct color. I allowed an orange flavored yellow to count as a yellow, or for Ikea blue to be too light or too dark. The image below shows the variation in color for Satyrbucks, which uses only green in the logo.
156 guesses at Starbucks green
At the far left, you see all 156 logos as drawn by the participants in the survey. (You can see a full sized version of this on Signs.com website.) In the middle drawing, I pulled all of the green pixels from each drawing, and averaged them together to show the green that the participant chose. At the far right, I show the 21 contestants that came within 10 DE2000 of the true Starbucks green. For reference, a common tolerance for commercial printing a color is 3.0 DE2000. Only two people out of the 156 participants were able to create a color from memory that would have been deemed acceptable printing of that logo.
Caveats...
I have made the assumption that there was an unbroken chain of proper color management throughout this process. If I had to put money on that, I would say that I would prefer to not put money on that. I don't say that to disparage Signs.com at all. I just know that the bar for rigor in Science is pretty high. But, looking at the middle image above... I rather doubt that any deficiencies in the rigor of this test could have caused that much variation in color.
Another caveat is in the interpretation of the results. This is a test of the participant's ability to recall the proper color from memory (as in Eddy's Coke red test), but also a test of the participant's ability to reproduce that color using the software provided. So, the logo drawing test is harder than the task of trying to find your favorite raisin bran.
Pondering
Eddy provided me with an interesting anecdote: "To put that unique Coke red in perspective: in the LinkedIn ‘Printing Production Professionals’ one of the printers that works for Coca-Cola shared that in the X-mas edition, the Coke red is slightly darker… (which I checked in my collection of Coke cans and it is correct) So if color is soooooo important, how does this different Coke red impact sales?"
I'm still kinda pondering why Eddy has a Coke can collection... but these two experiments beg the question about how precisely a brand color needs to be defined. Both experiments are well above the level of urban legend expressed by the statement "Color increases brand recognition by up to 80%". But neither experiment quite fulfills the high bar of rigor required to be accepted as peer-reviewed Science with a capital S. I don't expect to see either in the next edition of Color Research and Application.
But, the two experiments are suggestive, and that suggestion is a contradiction between the brand owner's expectations of what is needed and the psycho-physics of the color that we see.
In the next installment in this series, I will take a closer look at the Science that has been done, especially the Science having to do with our memory of colors. If you want a bit of a foretaste, look through the references below. I am going to pretend to have digested them in the next blog post.
References
Bae, G. L., M. Olkkonen, S. Allred, and J. Flombaum, Why some colors appear more memorable than others: A model combining categories and particulars in color working memory, J Exp Psychol Gen. 2015 Aug;144(4):744-63
Belcher, Teri, and Kevin Harvey, The Influence of Color, ANTEC 2007
Bartleson, C. J., Memory Colors of Familiar Objects, Journal of the Optical Society of America, Vol 50, No 1, Jan 1960
Burnham, Robert W., and Joyce Clark, A Color Memory Test, Journal of the Optical Society of America, Vol 44, No 8, Aug 1954
https://www.entrepreneur.com/article/233843
Cunningham, Meagan, The Value of Color Research in Brand Strategy, Open Journal of Social Sciences, 2017, 5, 186-196
Elliot, Andrew J., Color and psychological functioning: a review of theoretical and empirical work, Frontiers in Psychology, April 2015, Vol 6, Article 368
Goguen, Kate, The Influence of color on purchasing decisions related to product design, Master's Thesis, Rochester Institute of Technology, Feb 20, 2012
Javed, Saad Ahmed and Sara Javed, The impact of product’s packaging color on customers’ buying preferences under time pressure, Marketing and Branding Research 2(2015) 4-14
Kling, Axel, The Importance of Color Management for a Consumer Product Company, Printing Industries of America Color Management Conference, 2011
Patil, Daivata, Coloring consumer`s psychology using different shades the role of perception of colors by consumers in consumer decision making process: a micro study of select departmental stores in Mumbai city, India, Journal of Business and Retail Management Research (JBRMR) Vol 7 Issue 1 October 2012
Mohebbi, Behzad, The art of packaging: An investigation into the role of color in packaging, marketing, and branding, International Journal of Organizational Leadership 3(2014) 92-102
Morton, Jill, Color & Branding, Color Matters
https://www.colormatters.com/component/content/article/62-color-a-marketing/240-color-a-branding
Satyendra Singh, Impact of color on marketing, Management Decision, 2006, Vol. 44 Issue: 6, pp.783-789
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