electronic colour communication in the textile and ... - senai cetiqt
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Electronic colour communication in the textile and apparel
industry
Dr. Robert Hirschler Technical Advisor, Colour Institute, SENAI/CETIQT, Brazil
Abstract
Colour communication is of utmost importance in the textile and apparel industry, and
communicating numbers which describe colours unambiguously has been possible since the
international acceptance of the CIE system of colour measurement. Although today universally
used, instrumental colour measurement has its limitations. Advances in the calibration and colour
management of input, display and output devices have made it possible to communicate not only
numbers, but also high colour fidelity images. Non-contact colour measurement based on
controlled standard illumination and a calibrated camera opens new possibilities in the colour
specification of small, curved and patterned samples which could not be measured with
conventional instruments.
Keywords: Colour communication. Colour management. Colorimetry.
1 Introduction
The first attribute attracting a customer to select a piece of fabric or garment is its
colour. A pleasing colour or colour combination is one of the strongest sales weapons, but
the way the colour may be communicated from mind to market (i.e. from the designer to
the final customer) is a very complex one. There are different levels of colour
communication from the simple verbal through colour collections, colour order systems to
instrumental and virtual, each with their respective advantages and disadvantages
(HIRSCHLER, 2011).
Verbal colour communication can be very simple, using words like red, green, blue;
somewhat more detailed like dark red, grass green, sky blue, or very systematic, like
“light yellowish brown” (KELLY; JUDD, 1976), but it will always lack the precision needed
in industry. Using a colour sample collection (like Pantone) or a colour order system
(such as the Munsell system) helps, and the Munsell system even permits visual
interpolation between the samples in the colour collection, thus making it a useful tool in
non-instrumental colour communication. The most important characteristic of the Munsell
system is that it describes colour in perceptual terms Hue (what we generally call the
name of the colour: blue, green, yellow, orange, red etc.); Value (or lightness, i.e. lighter
– darker) and Chroma (which is similar to the saturation or the purity of the colour). The
colours are arranged in three dimensions as illustrated in Figure 1.
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Figure 1 – The MUNSELL colour order system as illustrated by the Munsell Color Tree Credits: Color Tree produced by Munsell Color Services® of X-Rite Inc. (www.munsell.com)
Instrumental colour communication had its beginning when the first industrial
spectrophotometer (the “Hardy”) became available in 1929, and two years later the CIE
(Comission Internationale de lÉclairage – International Commission on Illumination)
system of colorimetry was born. This made way of putting numbers on colours, but it
took over 30 years more for the first real electronic exchange of colour data to take place
in an industrial scale. In 1963 ICI announced a new service for the textile industry, called
IMP – Instrumental Match Prediction (ALDERSON et al., 1963), which today would be
called Computerized Colorant Formulation. The mere fact of calculating recipes by
computers wasn’t exactly new; the Davidson and Hemmendinger COMIC analogue
computer had been doing it since 1958, but there was a brand new concept in the
communication of colour data. The customer would measure the tristimulus values of the
required colour (the target) on a colorimeter, send them by telex (anybody still
remembers?) to the ICI Dyestuffs Division Headquarters in Blackley, and would receive
within a few hours the recipe for that particular colour. Figure 2. shows the computer
installation with the then extremely potent Elliott 803B computer (boasting 8 kilobytes of
computing capacity) in the back of the room, while to the left of the picture we can see
the communicating devices – the telex machines.
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Figure 2 – General view of the computer installation for the IMP system in Blackley Credits: ICI publication (1963) with kind permission of DyStar Colours Deutschland GmbH
By today’s standards we may smile at the level of the computer and communications
technology available in the early 1960’s, but it was all there for electronic colour
communication: specifying colours in numerical terms and sending digital data across
countries or continents.
2 Instrumental Colour Specification
For colours to be objectively communicated we need to put numbers on them, and there
are different sets of numbers with which colours may be specified.
Object colours can be measured by spectrophotometers, which will provide spectral
reflectance or spectral transmittance values. From the spectral reflectance values basic
colorimetric quantities, the X, Y, Z tristimulus values, may be computed in the CIE
system of colour measurement (SCHANDA, 2007) and these may be transformed into
CIE L*, a* and b* (CIELAB) coordinates which are related to the way we characterize
colours in visual (perceptual) terms. Tristimulus values (and, consequently, CIELAB
coordinates) may also be computed directly from the RGB values of digital input devices
such as digital cameras, scanners, or the RGB settings of monitors.
2.1 Spectral Reflectance Values
For object colours the spectral reflectance values may be considered their “digital
fingerprints” – they tell us which part of the visible spectrum is absorbed and which part
is reflected by the object. They are best understood by plotting reflectance as a function
of wavelength to get the spectral reflectance curves. On these diagrams the horizontal
axis shows the wavelength (i.e. the arrangement of the spectrum from violet and blue
through green, yellow and orange to red), and the vertical axis the relative amount of
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light reflected. Although they are difficult to be accurately interpreted in visual term there
is a certain relationship between the shape of the curve and basic visual characteristics.
Figures 3. to 5. illustrate the spectral reflectance curves of some selected samples from
the MUNSELL colour collection, one of the best known and most widely used colour order
systems.
Figure 3 – Spectral reflectance curves of selected MUNSELL samples of the same Value
and Chroma, differing only in Hue.
Differences in hue, as can be seen in Figure 3., are caused by the differences in the
shape of the curve. A red colour would have low reflectance in the blue and green region
of the spectrum and high reflectance in the red region. A yellow object would reflect both
green and red, and absorb mainly in the blue region. A pure green is characterized by
low reflectance in the blue and red regions, and high reflectance in the green region.
Figure 4 - Spectral reflectance curves of selected MUNSELL samples of the same Hue and
Chroma, differing only in Value (lightness).
Colours of the same Hue and Chroma have very similar curve shapes (Figure 4.), the
characteristic difference due to the differences in Value (lightness) are those of the
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height of the curve: the lighter the colour (higher Value) the higher the reflectance.
Figure 5 - Spectral reflectance curves of selected MUNSELL samples of the same Hue and
Value, differing only in Chroma.
Higher Chroma colours have steeper curves, the neutral grey (N6) is practically flat, the
highest chroma colour has the steepest curve (Figure 5).
It must be emphasized that these rules are very general. Spectral values in themselves
are not sufficient to communicate the visual appearance of colours, for this we have to
take into consideration the effect of illumination and the way a human observer sees
colours.
2.2 CIE Tristimulus Values and Metamerism
Colour is three-dimensional, we can describe any colour with three attributes such as the
MUNSELL coordinates Hue, Value and Chroma, or the ones used in the NCS system: hue,
white content and black content. The CIE system of colour measurement reduces spectral
data of objects into three numbers called tristimulus values in such a way that the
characteristics of the illumination and the way a human observer perceives colours are
also taken into consideration.
One set of X, Y and Z tristimulus values describes the colour of an object for one
particular CIE illuminant and one of the two CIE standard observers. In the calculation of
the tristimulus values we reduce the 16, 32 or more spectral reflectance values to three
numbers, thus we are necessarily losing some of the information. Whereas for any one
illuminant/observer condition there is only one corresponding set of tristimulus values,
the reverse is not true: there can be an infinite number of spectral curves (sets of 16, 32
or more spectral reflectance values) which yield the same X, Y and Z set (for that
particular illuminant/observer condition). These curves are said to define metameric
colours, which have identical tristimulus values (and therefore look exactly the same)
under a particular illuminant for a given observer, but have different tristimulus values
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(and therefore do not look the same) if either the illuminant or the observer (or both) are
changed, as illustrated in Table 1. and in Figure 6.
Table 1: Tristimulus values of two metameric grey specimens
Illuminant D65 / 10 degree observer
X1 = X2 = 27.0 Y1 = Y2 = 28.5
Z1 = Z2 =
30.4
Illuminant A / 2 degree observer
X1 = 31.1 Y1 = 28.5 Z1 = 10.1
X2 = 32.5 Y2 = 28.5 Z2 = 10.4
Figure 6 - Spectral reflectance curves of two metameric grey specimens, with tristimulus values as shown in Table 1.
The example shows the different reflectance curves of two grey specimens (Figure 6.)
with tristimulus values calculated for two illuminant/observer conditions (Table 1.) In
spite of the reflectance curves showing significant differences, the two colour appear
identical under illuminant D65 (standard daylight) for the 10 degree observer. If either
the illuminant or the observer changes, the two specimens will show a colour difference,
for example the one shown in Table 1. for illuminant A (tungsten light) and the 2 degree
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observer.
When communicating colours through tristimulus values (such as was the case of the IMP
system described in the Introduction) it must always be made very clear to which
illuminant and observer condition these values have been computed.
Although in the definition of the tristimulus values the way an average human observer
sees colours is incorporated, they do not describe colours in perceptual terms. We can
vaguely say that the X tristimulus value represents the red, the Y the green and the Z
the blue stimulus, but the most we can say with certainty is that when two sets of
tristimulus values are identical the colours appear to be the same, but if they are
different we cannot say (just by the tristimulus values) exactly how different the colours
are. For communicating these differences (and establishing tolerances) we need to
transform the X, Y and Z values into a colour space which describes perceptual properties
and differences better, notably into CIELAB coordinates.
2.3 CIELAB Coordinates, Colour Differences and Tolerances
The great advantage of the CIE 1976 L*a*b* (abbreviated CIELAB) object-colour space is
the similarity of the arrangement of colours to that of the Munsell space (see Figure 1.)
In CIELAB space the L* vertical axis represents lightness (from black in the bottom and
white on top); the a* axis is the direction of redness, -a* is greenness, b* yellowness
and –b* blueness. Colours of different hues (but constant lightness and chroma) are
arranged in concentric circles around the neutral (grey) colours in the middle.
CIELAB coordinates are calculated from the X, Y and Z tristimulus values, and thus the
same restrictions apply to them: they are always representing one illuminant / observer
condition; from the same set of spectral reflectance values we get different sets of L*, a*
and b* coordinates if we change either the illuminant or the observer (or both). For
metameric colours it is also true (as for the XYZ values) that CIELAB coordinates are the
same in spite of different spectral reflectance values (for one illuminant / observer
condition).
In recent years communicating colours through CIELAB values has gained special
importance in the digital world, as we shall see later (in Section 3.1), because they are
device independent (as opposed to device dependent RGB or CMYK values). In the textile
and apparel industry CIELAB values themselves are rarely used, their special importance
lies in their application for the evaluation and communication of colour differences.
We have already mentioned that tristimulus values (and their differences) are not good
descriptors of colour differences; CIELAB is preferred for this application. In CIELAB
space we can calculate the distance between the points representing different colour
stimuli, this distance is called the colour difference, usually designated as *abE∆ . We
should mention here that there are other colour coordinate systems still in use (albeit
obsolete) having L, a, b coordinates (Hunter, Adams-Nickerson) and DE as colour
difference; and the use of the asterisk is important to designate CIELAB not only for
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*abE∆ but also for the individual L*, a* and b* coordinates. The total colour difference
*abE∆ may be split into its components of *L∆ (lighter-darker) and either *a∆ (redder-
greener) and *b∆ (yellower-bluer) or *abC∆ (chroma difference) and *
abH∆ (hue
difference). (The ab index is important to distinguish these quantities from their
analogues in the CIE L*u*v* system).
Communicating colour differences is of primary importance in the textile and apparel
industry, because that is how we may decide if a product conforms to the colour
specifications within tolerance. Very soon after the introduction of the CIELAB system it
was confirmed that although it is a very useful tool it is not very efficient in making single
number pass/fail decisions (colour tolerancing). In the textile industry one of the most
difficult questions is “is this colour difference commercially acceptable or not”. Ideally we
could say: “if the measured colour difference is less than 1 *abE∆ unit, it is acceptable, is
it’s more, then not”. Unfortunately, we are very far from this ideal situation, for two
reasons.
The first reason is that CIELAB (good as it is) is not entirely uniform across colour space,
and for the same article and the same client the size of the acceptable colour difference
varies not only from colour to colour (i.e. it is different for reds and blues, light and dark
colours etc.) but depends also on the direction of the difference (i.e. it may be larger if
the difference is in lightness, but smaller if it is in hue). One solution for this problem is
to set up individual tolerances for every colour (generally around selected colour points),
but in industry it is well-nigh impossible. The best solution, however, is to calculate from
the CIELAB coordinates colour differences according to one of the colour tolerance
equations (CMC or CIEDE2000) which “distort” colour space in such a way as to conform
better to the visual judgement of trained professionals.
The other reason why we cannot give only one number as THE acceptable colour
difference (be it CIELAB, CMC or CIEDE2000) is that the decision whether a given colour
difference is acceptable or not is as much of a commercial as a technical question. The
size of the acceptable colour difference (the tolerance limit) depends on the product and
on the end-users demands. Generally the tolerance limits for fabrics sent to retail are
more generous than for the same fabric sent to a large garment manufacturer. The
tolerance for a fashion article may be more lenient than for uniforms. Also, customers
willing to pay more may demand stricter tolerances.
In the SENAI/CETIQT Colour Institute we have, over the years, performed over 50,000
pass/fail evaluations in nearly a dozen textile companies, and compared the judgement
of a group of trained colourists to the instrumental evaluation utilizing different colour
tolerance formulae (GAY; HIRSCHLER, 2002; GAY; HIRSCHLER, 2003).
We have found that for one company the tolerance limit had to be set as tight as DECMC =
0.7 and for another it could be as high as DECMC = 2.3 to agree with the judgement of the
visual panel of 8 to 12 observers (considered to be the “right” decision). If tolerance
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limits were established individually for each major product group and either the CMC or
the CIEDE2000 formula was used the percentage of right instrumental decisions was
around 80%. This is somewhat better than the percentage of right individual visual
decisions.
We may conclude that colour differences (or rather, tolerances) may be communicated
with high precision, provided that an adequate colour difference formula is used and the
necessary preliminary work of establishing acceptability limits for the given product
group has been correctly done.
2.4 Communication between Colour Measuring Instruments
Many people take it for granted that once a colour is measured (i.e. we can express it in
numbers, be they spectral reflectance, X, Y, Z tristimulus or CIELAB values, or some kind
of colour difference) we may freely communicate it, and the result will be perfect
understanding of exactly which colour we mean. Unfortunately, this is not so. There are
always some differences between measurements made on different instruments, even if
we make sure that they are in perfect operating conditions. The performance of colour
measuring instruments may be specified following the ASTM Standard Practice for
Specifying and Verifying the Performance of Color-Measuring Instruments (2008). In the
Colorimetry Laboratory of the SENAI/CETIQT Colour Institute different sets of Ceramic
Colour Standard tiles (CCS) are used for verifying instrument performance. The master
sets have been calibrated by the National Physical Laboratory (NPL) in Teddington, UK.
Repeatability shows how well the readings of an instrument are repeated over a short or
medium term. In modern industrial instruments in good conditions repeatability is
excellent; it is in the order of a few hundreds of *abE∆ units, more than one order of
magnitude below the perceptibility level.
Reproducibility may refer to inter-instrument agreement (a form of reproducibility in
which two or more instruments from the same manufacturer and model are compared)
or to inter-model agreement, a form of reproducibility in which the measurements of two
or more instruments from different manufacturers are compared. Needless to say in the
latter case the comparison only makes sense if the instruments (albeit of different
models) have the same measurement geometry. From the point of view of colour
communication reproducibility is extremely important, because we are generally
comparing measurement results from different locations, if possible between instruments
of the same model, but this is not always the case. Inter-instrument agreement for top-
of-the-line instruments may be as good as 0.15 to 0.3 *abE∆ units (average obtained on
the CCS set), i.e. just below or around the visual perceptibility limit. Inter-model
agreement may be much worse, around 0.5 *abE∆ units or more, which may be
considered above the perceptibility limit, and in the order of what is often considered the
industrial acceptability limit.
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It makes very little sense to communicate colours with such large uncertainty, and the
manufacturers have tried different methods to improve repeatability. In the 1990’s
Datacolor International launched what was called their “close tolerance” (CT) models of
the SF 500 spectrophotometer. Our laboratory exchanged data with two other
laboratories in the United States, and for the set of CCS we got inter-instrument
agreement of around 0.15 *abE∆ units, perfectly acceptable for colour communication. In
order to make advantage of this type of arrangement the users had to purchase the
same model of instruments, preferably within a reasonably short time period.
Datacolor has since improved and extended the method which is now incorporated in the
company’s Maestro package (utilizing a set of CCS tiles provided by Datacolor). A similar
method, the NetProfiler, was launched in the late 1990’s by GretagMacbeth (now X-Rite)
which makes use of the potential offered by communicating through the Internet.
Vasconcellos (2001) compared the performance of a Datacolor SF600 with a
GretagMacbeth CE7000 and found that the inter-instrument agreement was reduced
(improved) from 0.36 *abE∆ to 0.094 *
abE∆ through profiling with the NetProfiler. Datacolor
(2010a) published a study on the Internet comparing the two methods, and arrived at
the following highly interesting conclusions:
• Both profiling programs were able to improve the inter-instrument agreement on
BCRA tiles, but with consistency only for their own instruments;
• Improvements in BCRA agreement did not produce similar improvements in textile
agreement;
• The instrument diagnostic routines of the two profiling programs did not identify
problems with the non-native instruments.
Their final conclusion was that
These tests confirm that improvements in inter-instrument agreement gained
through profiling of a properly functioning spectrophotometer are insignificant
when compared to the error introduced by poor measurement technique, changes
due to sample conditioning, and operator error.
3 Virtual Colour Communication
Communicating colours by numbers is all very well, but what would really be nice is to
show you on your end of the line what I see here on my end – which is what virtual
colour communication is all about. Nowadays this appears to be very simple: I have a
digital camera or a scanner, enter the colour (or a complex design of many colours) into
some software, send the file to you over the Internet, and you just see it on your monitor
or print it out on your printer. Right? Wrong! As we shall see, there is more to it than
meets the eye (literally). If you ever tried to compare the image you have on the monitor
to the original you have just scanned in, or compare the print from your printer to the
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monitor image, or the original you were very likely most disappointed – unless you are
using colour management.
3.1 Device dependent colour
In today’s world of digital imaging we can’t really get even acceptable colour
reproduction across the media without colour management, and yet, it is a technique not
at all well known, and even less well understood. Why do we need colour management?
To understand this rather complex problem we have to first think about the different
ways colours are produced (Figure 7.)
0
10
20
30
40
50
400 450 500 550 600 650 700
Ref
lect
ance
(%)
Wavelength (nm) Figure 7 - Object colours (left); additive mixing (middle) and subtractive mixing (right)
Object colours are produced by the selective absorption and reflexion across the visible
spectrum depending on the combination of colorants used, and this is the “original” what
we scan in or take a photo of. Monitors, scanners and digital camera work in a different
way. They mix colours from three additive primaries red, green and blue (hence RGB),
which works because human colour perception is also based on RGB primaries, thus
additive mixing obeys the laws of psychophysics. Office printers produces colours by
subtractive mixing (obeying the laws of physics) based on three subtractive primaries
yellow, magenta and cyan (hence YMC). For technical reasons the great majority of these
printers uses a fourth colour, black (K), and therefore we usually speak of the YMCK
system. Here we are back again to an object colour, prints can be characterized by their
spectral reflectance curves, but these will inevitably be very different from those of the
originals. Even if we got a perfect match between original and print (for one illuminant /
observer condition) these colours would be highly metameric. (We may remark here that
some of the most recent digital textile printers work with 6 or 8 inks, and there the
resulting colour will be the combination of these, and thus metamerism may be reduced.)
Let’s see a real world example of how colours are produced on two monitors (from the
same scanned RGB values) and on two printers (from the same Photoshop file) as
compared to the original.
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0
10
20
30
40
50
400 450 500 550 600 650 700
Ref
lect
ance
(%)
Wavelength (nm)
OriginalLaserWax
0
10
20
30
40
50
60
400 450 500 550 600 650 700
Rad
ianc
e (W
/sr c
m²n
m)
Wavelength (nm)
LCD
CRT
Figure 8 – Spectral reflectance curves of a grey original and two prints (left) and the same grey as it appears on a CRT and an LCD monitor (right).
We can see on the left of Figure 8. that although the original neutral grey is reasonably
well reproduced by the laser printer, there is a high degree of metamerism between the
two. The wax printer printed the same grey much lighter, and the colour is also highly
metameric both to the original and the laser print. On the right we see how the two
monitors produce the same colour: here we can detect significant colour difference, and
even if the colours were adjusted to appear the same there would be a high degree of
metamerism.
As if these basic differences between the way colours are formed were not enough, there
can be (and generally there are) great differences even between similar devices. A
monitor can “understand” instructions given in RGB values. When we scan in the same
colour on different scanners, they will provide three more or less different sets of RGB
values – how can we then expect the monitor to show the same colours? And if we have
another monitor, which will interpret the same RGB value set in a different way, so we’ll
end up with different colours on the two monitors even if we enter the same RGB values,
as illustrated in Figure 9.
Figure 9 – Uncalibrated input and output devices result in unmanaged colours. Colours are for illustration purposes only; the colour differences shown are vastly exaggerated
Similarly, even when we give the same CMYK instructions to different printers the output
will be different (just as it happened in our example of the laser and the wax printers).
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We say that RGB and CMYK instructions are device dependent, and to deal with them we
have to apply colour management.
3.2 Colour Management
According to Wikipedia (2010) “In digital imaging systems, colour management is the
controlled conversion between the colour representations of various devices, such as
image scanners, digital cameras, monitors, TV screens, film printers, computer printers,
offset presses, and corresponding media.”
In a simple case we have one input device (scanner or digital camera), a display device
(monitor) and an output device (printer). By carefully studying the characteristics of each
device each can be “calibrated” against the other, and thus the monitor and the printer
shall show the same colours as present in the scanned original as illustrated in Figure 10.
Figure 10 – Closed-loop colour-managed system where each device is individually
calibrated against each other device
This is a lot of work; each new device that we wish to use in the system has to be
individually calibrated against all the others. This closed loop colour control becomes
unmanageable if there are more input, display and output devices. The solution is to
create one central “switchboard” called the profile connection space, which is
implemented in the open-loop systems.
In the open-loop system there is no “calibration” between devices, a general purpose
profile is created for each device, which communicates the device characteristics to the
connection space, and receives the information from other devices also in this
standardized form. For this to work we need to convert (through profiling) the device-
dependent RGB or CMYK values into device-independent CIELAB colour space.
To explain how a profile works we may take the example of three scanners, which
produce three sets of (device dependent) RGB values for the same red colour. Device
profiling finds the equivalent CIELAB values for each, and the whole point of colour
management is that from three different sets of RGB values (which represent the very
same colour) we should arrive at one set of (device independent) CIELAB values as
illustrated in Figure 11. By the same token if we send the (device independent) CIELAB
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values to different output devices (monitors, printers), they would each convert it back
into their (device dependent) RGB resp. CMYK values, and thus produce the same colour
as was scanned in from the original.
Figure 11 – Device dependent RGB values are converted into device independent CIELAB
values through the device profiles. 3.3 Virtual Colour Communication in the Textile and Apparel Industry
In the previous section we have seen how digital colour communication works beyond
traditional, spectrophotometry-based colorimetry. New technologies in image capture and
processing together with the technology of colour management have made it possible to
communicate not only the colour of relatively large uniform spots (which may be
measured on a conventional instrument) but also that of complex images.
Virtual colour communication can also be helpful where traditional colorimeters or
spectrophotometers have their limitations. Conventional instruments cannot measure
very small specimens (such as pieces of yarn), multicoloured or non-uniform surfaces
such as highly structured lace and multicoloured pile fabric. In order to overcome these
limitations digital imaging systems (cameras or scanners) may be used which capture the
total colour and appearance of 2D and 3D objects including those with irregular, curved
or non-uniform surfaces. Colours are characterised by the image formed on a monitor
and/or by the colorimetric values RGB. In this case the quality of the illumination
(irrelevant in spectral measurements) is of utmost importance, and in order to ensure
the repeatability and reproducibility of the measurements well defined and well controlled
illumination is necessary.
In the early days imprecise on-screen colour was considered the weakest link in the
application of virtual colour in the production chain. In the textile and apparel industry
the first attempts to improve the precision of on-screen colour for image communication
go back to the 1980’s when the first CAD systems were started to be used. Researchers
at UMIST (HAWKYARD; OULTON, 1991) and also of the LUTCHI Research Centre at the
University of Loughborough (LUO et al., 1992) developed closed loop control systems for
textile and apparel applications. The UMIST research resulted in the ShadeMaster system
first marketed by a textile CAD company called Textile Computer Systems (TCS) Ltd. The
ShadeMaster simulated object colours as shown under standard D65 illuminant (Figure
12.) and, in addition to monitor and printer calibration, it specified colours according to
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CIE X, Y and Z tristimulus values, CIELAB coordinates and also generated synthetic
reflectance curves.
Figure 12 – Coloured objects shown under simulated D65 standard illuminant in the TCS
ShadeMaster system Credits: Photos by the author, 1991
In 1996 UMIST developers founded Colorite (U.K.) Ltd., and sold the product as
ImageMaster. In 1997, Datacolor acquired Colorite, and ImageMaster became
ENVISIONTM, which is still part of the Datacolor suite of colour communication software
with image separation; colour creation and selection; spectrophotometer input; colour
visualization under various lighting conditions; monitor calibration, printer calibration;
colour library with search and retrieve functions (DATACOLOR 2010b).
ChromaShare (2010) have extended the concept of colour management well beyond the
definition given above in Section 3.2. Their modular SmartClient Colour Management
Software covers all the most important functions a user may need for visual colour
communication:
• CS PaletteShare - measure, save, search and edit palettes and libraries of colours;
• CS ImageShare - add full image separation and analysis features;
• CS WorkFlow - initiate and manage colour requests from your supply chain.
Figure 13. shows how images, parts of images, palettes and colour data may be
communicated between supplier and client.
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Figure 13 – Screen shot from the ChromaShare ImageShare module
Credits: Courtesy of Andrew Bennett, ChromaShare
As the software works over the Internet users can share the same colour experience
from anywhere in the world, enabling enterprise-wide colour management and decision
making. Images, graphics, and production data, can not only be readily exchanged, but
visualised too. The user’s web site can be enabled so that his customers can see the
palettes and images and graphics that he wants to be shared. Global colour management
means that different RGB versions of palettes, artwork and photos are sent to each user,
depending on their monitor - but they all share the same, calibrated visual result!
One of the most complex issues of colour communication in the textile and apparel
industry is that of working with images of structured textile substrates, where the
structure fundamentally influences the apparent colour. Traditional colour measurement
cannot cope with this situation, and until recently transmitting complex images in true
colour proved to be extremely problematic. LUTCHI researchers (LUO et al., 1992)
developed a commercial product called ColourTalk in which computer-based colour
models have been implemented, integrated with a viewing cabinet for visual colour
matching tasks and a networked spectrophotometer for colour measurement. The
research was later transferred to the University of Derby’s Colour & Imaging Institute
(RHODES; LUO, 1996) where the concept of a new product, the DigiEye was developed
(LUO, 2006).
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Figure 14 – The DigiEye system for high-precision virtual colour communication
Credits: Photo courtesy of VeriVide Ltd.
Figure 15 – The DigiEye Large Area Imaging Cabinet can communicate the image and the
colour of entire pieces of garment Credits: Photo courtesy of VeriVide Ltd.
The DigiEye system is based on controlled illumination (simulating the CIE D65 standard
illuminant), a digital camera and a monitor colour managed to provide images with a
high degree of colour fidelity (Figure 14.) The simulated D65 illumination may also be
provided in a large area format making it possible to communicate images (and colours)
of pieces too large for the standard DigiEye box (Figure 15.)
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Virtual colour communication with such a system may consist of transmitting the
“calibrated” image, where the colours at the receiving end (calibrated monitor or printer)
are very nearly identical to the originals. Showing the full image has the advantage that
in the case of patterned images (such as prints, jacquards etc.) not only the individual
colours but also the interactions among them are communicated. The DigiEye system
also has a module called DigiPix for the non-contact measurement of those specimens
which are too small, patterned, curved or otherwise unsuitable to be measured on
conventional instruments. In this case the measurement is based on RGB values
provided by the calibrated digital camera, under standard illumination and converted by a
special algorithm into synthetic reflectance curves which may be used for full colour
communication e.g. fed into a recipe prediction system.
4 Conclusion
Electronic colour communication has come a long way since the introduction of
instrumental colour measurement. It started with the simple transfer of colorimetric data
over whatever means of communication was available in those days and has evolved into
the instantaneous transmission of images and the possibility to visualize them with high
colour fidelity on a monitor or printer. Electronic colour communication facilitates
information processing from the designer to the final consumer wherever the participants
of the production chain be located.
5 References
ALDERSON, J. V. The practical exploitation of Instrumental Match Prediction. J.S.D.C., Bradford, v.
79, n. 12, p.723-730, Dec. 1963.
ASTM E2214-08: Standard Practice for Specifying and Verifying the Performance of Color-
Measuring Instruments. West Conshohocken, PA, 2008.
CHROMASHARE innovative colour management software. Disponível em:
<http://www.chromashare.com>. Acesso em: 13 Aug. 2010.
DATACOLOR. Reflectance in perspective “Will Instrument Profiling Give Me Better Measurements?”
Disponível em:
<http://www.datacolor.com/learningarticles/Reflectance%20in%20Perspective.pdf>. Acesso em:
13 Aug. 2010a.
DATACOLOR. ENVISIONTM software description. Disponível em:
<http://www.datacolor.com/software/envision/>. Acesso em: 13 Aug. 2010b.
DIGIEYE. Disponível em: <www.digieye.co.uk>
GAY, Jennifer K.; HIRSCHLER Robert. Determination of industrial colour tolerance limits. Case
studies in the textile industry. In: CONGRESS OF THE INTERNATIONAL COLOUR ASSOCIATION
(AIC), 9., Rochester, NY, 2001. SPIE v. 4421, p. 646-649, 2002.
GAY, Jennifer K.; HIRSCHLER Robert. Field trials for CIEDE2000. Correlation of visual and
instrumental pass/Fail decisions in industry. In: INTERNATIONAL COMMISSION ON ILLUMINATION
SESSION OF THE CIE, 25., San Diego, 2003. Proceedings. v. 1, p. D1/38-41.
HAWKYARD, Chris J.; OULTON, David P. Colour in textile computer-aided design systems. J.S.D.C.,
Bradford, v. 107, n. 9, p.309-313, Sept. 1991.
R. Hirschler REDIGE v. 1, n. 1, 2010
_______________________________________________________________________________
www.cetiqt.senai.br/redige │ 61 │
HIRSCHLER, Robert. Colour communication in industry from design to product - with special
emphasis on textiles. In: PARRAMAN, Carinna. Colour Coded. Bradford: Society of Dyers and
Colourists, 2011. (in preparation).
ICI Instrumental Match Prediction. Manchester: ICI Dyestuffs Division, 1963.
KELLY, Kenneth L.; JUDD, Deane B. Color Universal language and dictionary of names.
Washington, D.C.: National Bureau of Standards, Department of Commerce, A-7, 1976
LUO, M. Ronnier. Applying colour science in colour design. Optics & laser technology.
Amsterdam, v. 38, n. 4/6, p. 392–398, June-Sept., 2006.
LUO, M. Ronnier et al. Effective colour communication for industry. J.S.D.C., Bradford, v. 108, n.
12, p. 516-520, Dec. 1992.
RHODES, Peter A.; LUO, M. Ronnier. A system for WYSIWYG colour communication. Displays.
Amsterdam, v. 16, n. 4, p. 213-221, May 1996.
SCHANDA, János. CIE colorimetry. In: SCHANDA, János. Colorimetry: understanding the CIE
System. Hoboken: John Wiley & Sons, 2007. 25-78
VASCONCELLOS, James R. Supply chain management of color & appearance: evolution vs.
revolution. AATCC Review. Research Triangle Park, N.C., USA, v. 1, n. 6, p. 14-18, June 2001.
VERIVIDE. Disponível em: <www.verivide.com>
WIKIPEDIA. Disponível em: <http://en.wikipedia.org/wiki/Color_management>. Acesso em: 13
Aug. 2010.
X-RITE. Munsell Color. Disponível em: <http://www.xrite.com/top_munsell.aspx>
Biographical Notes
Robert Hirschler graduated at the Technical University of Budapest
(Hungary) in textile chemistry and has been involved in textile dyeing,
finishing and applied colorimetry for over 40 years. He has been working
for CETIQT as technical advisor since 1988, for 15 years as a consultant of
the United Nations Industrial Development Organization. He is Chair of
two CIE Technical Committees: TC1-44 (Practical Daylight Sources for
Colorimetry) and TC1-77 (Improvement of the CIE Whiteness and Tint
Equations) and also that of the AIC Study Group on Colour Education. Email address: [email protected]
Curriculum at Lattes Platform: http://lattes.cnpq.br/0080512317658424