Use of DeviceLink Profiles for graphic industries ... - Impressed

Use of DeviceLink Profiles for graphic industries 

TRICHON Amélie – PFE 2006/2007 1

SUMMARY 


Abstract 6 

1 PART ONE: BIBLIOGRAPHIC RESEARCH 7 

1.1 Colorimetry basic knowledge 7 

1.1.1 Color and vision 7 

1.1.2 CIE color systems 7 

1.1.3 Definitions of �E 10 

1.2 Color Management 13 

1.2.1 Why do we need Color Management? 13 

1.2.2 Definition 14 

1.3 ICC Profiles 14 

1.3.1 ICC organization 14 

1.3.2 Definition and interest 14 

1.3.3 Rendering intent 15 

1.3.4 Content of an ICC profile 16 

1.3.5 How to create an ICC Profile 17 

1.4 ICC DeviceLink Profiles 18 

1.4.1 Definition 18 

1.4.2 Advantages 18 

1.4.3 Disadvantages 18 

1.5 Dynamic DeviceLink Profile (Dynamic DVLP) 19 

1.5.1 Defintition 19 

1.5.2 Advantages and disadvantages 19 

1.6 Black generation 19 

1.6.1 UCR and GCR 19 



1.6.2 Alwan “Dynamic Maximum Black” generation 20 

2 PART TWO: EXPERIMENTAL STUDY 22 

2.1 Software used 22 

2.1.1 Alwan LinkProfiler 22 

2.1.2 Alwan CMYK Optimizer 26 

2.1.3 Alwan ColorPursuit 29 

2.2 Tests and Works 30 

2.2.1 Description of the project 30 

2.2.2 Part 1: Processing of Tiff images 33 

2.2.3 Part two: test on PDF 40 

3 CONCLUSION 53 

4 BIBLIOGRAPHY 55 

5 GLOSSARY 57 

6 ACKNOWLEDMENTS 58 

KEY WORDS 59 

ILLUSTRATIONS AND TABLES 

Figure 1-1: The CIELAB color space 10 

Figure 1-2: Color management proposes a way to connect all devices in a graphic 

chain through a common space 13 

Figure 1-3: The architecture of a color management system 14 

Figure 1-4: The four ICC rendering intents. On these examples the triangle 

represents input device gamut and the circle represents the output device gamut. 15 

Figure 1-5: The ISOwebcoated profile structure 16 

Figure 1-6: Structure of the tables in a profile. The information provided in the 

header indicates which table should be used (0, 1 or 2). 17 

Figure 1-7: On this example, 50% of CMY inks are replaced by 30% of black ink. 



Next the color will be completed by a new percentage of CMY inks. 19 

Figure 2-1: screenshots of LinkProfiler interface, arrows show the variable 

parameters in the tests 24 

Figure 2-2: Alwan Color Hub interface 25 

Figure 2-3: ICC Profile Processor interface 25 

Figure 2-4: CMYK Optimizer Task interface 27 

Figure 2-5: CMYK Optimizer DTAC tab interface 27 

Figure 2-6: CMYK Optimizer Purity tab interface 28 

Figure 2-7: CMYK Optimizer Vector interface 28 

Figure 2-8: CMYK Optimizer Action tab interface 28 

Figure 2-9: ColorPursuit interface 29 

Figure 2-10: In this example, the original file (n°1) is in ISOcoatedv2 color space 

and the transformed file (n°2) in ISOwebcoated color space. 29 

Figure 2-11: Image Comparator window calculates �E difference between 2 images 30 

Figure 2-12: Twenty color transformations were used to assess probable situations 31 

Figure 2-13: CMYK Optimizer “Check Only (Preflight)” action 32 

Figure 2-14: test files 33 

Figure 2-15: Schematic diagram of image files processing workflow 34 

Figure 2-16a: Average �E on VPR 36 

Figure 2-17b: Maximum �E on VPR with medium GCR 36 

Figure 2-18c: % of output colors within �E 4 on VPR images 37 

Figure 2-19: Average �E for TC 3.5 chart with medium GCR 38 

Figure 2-20: �CMYK on TC 3.5 with the GCR (2.4) settings (series 1) 40 

Figure 2-21: PDF test form created for the tests (left: page1; right: page2) 41 

Figure 2-22: PDF test form elements 41 

Figure 2-23: PDF testform processing workflow 42 

Figure 2-24: Medium �E 94 on car image from PDF test form with “color matching” 

settings 44 

Figure 2-25: Medium �E 94 on car image from PDF test form with “Dynamic 

Maximum Black” settings. 44 

Figure 2-26: Medium �E 94 on TC 3.5 from PDF test form with “color matching” 

settings 45 

Figure 2-27: Medium �E 94 on Medienkeil from PDF test form with “color matching” 

settings 45 



Figure 2-28: Medium �E 94 on TC 3.5 from PDF test form with “Dynamic Maximum 

Black” settings 46 

Figure 2-29: Medium �E 94 on Medienkeil from PDF test form with “Dynamic 

Maximum Black” settings 46 

Figure 2-30: Positions of the test points on images from page 1 of PDF test form 47 

Figure 2-31: Medium �E on images from page1 of PDF test form with “color 

matching” settings 48 

Figure 2-32: Medium �E on images from page1 of PDF test form with “Dynamic 

Maximum Black” settings 48 

Figure 2-33: Results of ink saving test on PDF test form for “color matching” 

settings 49 

Figure 2-34: Results of ink saving test on PDF test form for “color matching” 

settings 49 

Figure 2-35: Results of ink saving test on PDF test form for “Dynamic Maximum 

black” settings 50 

Figure 2-36: Results of ink saving test on PDF test form for “Dynamic Maximum 

black” settings 50 

Figure 2-37: Results of ink saving test on car image from PDF test form for “color 

matching” settings 51 

Figure 2-38: Results of ink saving test on car image from PDF test form for 

“Dynamic Maximum Black” settings 51 


Abstract 


Nowadays ICC color management is widely used. The majority of graphic industry 

operators use ICC profiles to achieve predictable and consistent colors. Nevertheless, 

ICC Device Profiles transformations do not take into consideration some printability 

issues. 

An ICC Device Profile transformation can convert a source CMYK file to another CMYK 

destination space for example. Input CMYK pixel values are converted to LAB using the 

source device profile and then the obtained LAB values are converted to destination 

CMYK values by means of the destination device profile. This operation can create 

problems on the press because specific information can be lost. For example, an 

original black text will be converted in four colors (CMYK) like all the others elements 

of the document, which will complicate registration on press. To avoid this kind of 

printability related problems, ICC DeviceLink Profiles (DVLPs) can be used. This is a 

specific type of ICC profiles, which is built by connecting/concatenating two ICC 

Device profiles. By using DeviceLink profiles (DVLPs) we can apply color 

transformations on CMYK data while paying attention to printability parameters like 

color purity, TAC (Total Area Coverage), black generation etc... 

ICC DVLPs (DeviceLink Profiles) have been used by the printing industry for some years 

now to repurpose files in order to adapt incoming data to the actual printing process 

properties and requirements. These profiles contain a predefined color transformation 

which s applied to all files and files content without differentiation. 

Recently, Dynamic DVLP technology has been introduced in the printing industry. 

Dynamic technology differ from Static (Conventional) DVLP technology in that it takes 

into account source file content prior to building the optimal DVLP needed for the 

defined color transformation. A Static DVLP applies the same transformation to all 

files whereas a Dynamic DVLP will check the content of the file and then optimizes it 

depending on its content. 

The aim of this project is to study the interest of Dynamic DVLPs compared to Static 

(Conventional) DVLPs. This study will try to determine if the technological advance of 

Dynamic profiles is real or not. The project will look at the benefit of using each type 

of DVLPs for printers in terms of color matching, print contrast and ink savings. 



1 PART ONE: BIBLIOGRAPHIC RESEARCH 

This chapter deals with color management and ICC profiles. It explains what color 

management is, why we need to use a color management system when we want to 

reproduce colors and how it functions. Here you will find the definitions of an ICC 

profile, a Static (Conventional) DeviceLink profile (DVLP) and a Dynamic DeviceLink 

profile (DVLP). It is essential to be familiar with this part to understand the aim of the 

project and the practical studies realized. 

To start, it is important to know how color is created and perceived by human eye so 

in the first part, you will find reminders about basic colorimetry and color vision. [1] 

1.1 Colorimetry basic knowledge 

In color science, a color is called a color stimulus; it is the result of the combination of 

three parts: the light source, the object which reflects the light and the human 

observer which receives the light. We can specify each part of the vision process by its 

spectral power distribution. In this section you will find the description of the role that 

the human eye plays in color perception and the methods to measure colors. 

1.1.1 Color and vision 

A color stimulus is characterized by its spectral power distribution which is the 

product of the spectral power distribution of the light source and the spectral 

distribution of the object. But the human eye does not make the difference when it 

perceives a light which is a mixture of several wavelengths and can not analyze the 

signal wavelength-by-wavelength. Indeed the human vision system is only sensible to 

wavelengths between about 400 and 700 nanometers and is based on three kind of 

retinal photoreceptors (cones), which are sensible to different wavelengths, 

respectively short, medium and long. This phenomenon explains that we can 

reproduce any color from a mixture of three primary lights (in appropriate quantities), 

red, blue and green. So a “standard observer” can decompose a trichromatic signal to 

obtain a fourth color. Moreover, the trivalence nature of color vision shows that it is 

possible to perceive the same color sensation with two color stimuli having different 

spectral components. In this case, the two color stimuli produce the equivalent effects 

on the photoreceptors and the two colors appear identical. This is called metamerism. 

This phenomenon explains why we need to quantify and measure a color very 

accurately. This is why the CIE (Commission Internationale de l’Eclairage) defined 

different systems to describe a color. 

1.1.2 CIE color systems 

The first mathematical model proposed by the CIE, is based on three monochromatic 


primary lights: red, green and blue, fixed arbitrarily. 

LR= 700 nm 

LG= 546 nm 

LB= 436 nm 


But in this model, the white is an equal mixture of the three components but we 

determine visually that theoretical white point is: 

1.1. 

R= 1.2. 1 Cd/m� 

1.3. 

G=4,591 Cd/m� 

B= 0,059 Cd/m� 

So we determine the RGB system: 

1.4. 

R= 1.5. LR 

1.6. 

G= LV/ 4,59 

B= LB . 0,059 

This system is practical because based on real mechanism of vision but it leads to 

negative components especially for very saturate colors. So in 1931, the CIE decide to 

create a new system with imaginary components that are linear functions of the RGB 

system. 

This is the CIE XYZ 1931 system: 

X= 2.77 R +1.74 G + 1.128 B 

Y= R + 4.59 G + 0.06 B 

Z= 0.0046 G + 5.58 B 

These tristimulus values can also be describe differently by summing the products of 

the object and light source over the visible wavelengths (�): 


X = � �� R�x �d� 

� � visible 

Y = � �� R� y �d� 

� � visible 

Z = � �� R�z �d� � � visible 


where R� represents the spectral reflectance of the reflective object; �� the spectral 

power distribution of the light source and X �, Y� and Z � the color matching functions 

of the CIE Standard Colorimetric Observer. 

Then we try to build mathematic systems, which can be represented graphically and 

where the distance between two colors represents the colorimetric distance. 

If we want representing the XYZ components we need to calculate the reduced x and y 

coordinates: 

X 

x = 

X + Y + Z 

Y 

y = 

X + Y + Z 

But the CIEXYZ 1931 system has disadvantages. Indeed the calculated colorimetric 

differences do not match with the real difference perceived by the observer. The 

research allows adjusting other systems, more adapted. Among these, we can quote 

the CIELuv or the CIEL * a * b * 1976, the most used currently. 

The CIEL * a * b * 1976 system is built from the CIEXYZ 1931 system: 

L * = 116 Y � � 3 

� � � 16 

� Yo� 

a * = 500 X 

1 

� � 3 

� � � 

� Xo� 

Y 

� 

1� 

� � 3 

� 

� � 

� 

� � Yo� 

� 

� 

� 

b * = 200 Y 

1 

� � 3 

� � � 

� Yo� 

Z 

� 

1� 

� � 3 

� 

� � 

� 

� � Zo� 

� 

� 

� 

1 

With X,Y,Z the CIEXYZ1931 coordinates of the color and Xo,Yo,Zo the CIEXYZ 1931 

coordinates of the light source. 



Finally, the “sister” of the CIELAB system is known as CIELCH system where LCH means 

Lightness, Chroma and Hue. L * has the same signification than in CIELAB. C * is the 

chroma coordinate, more the point is far from the center and more the color is 

saturated. And h is the hue angle, expressed in degrees where 0° is for red, 90° for 

yellow, 180° for green and 270° for blue. 

CIELAB and CIELCH systems share the same color space. The only difference is that 

CIELAB specifies a position on a rectangular grid, although CIELCH use cylindrical 

coordinates. 

L * = L * LAB 

C ab 

1 

* * * 2 = (a + b ) 

hab * = arctan( b* 

) 

* 

a 

1.1.3 Definitions of �E 

Figure 1-1: The CIELAB color space 

So, we have shown that there are many different ways to describe and quantify a 

color. That is why there are also several different methods to quantify a difference 

between colors. 


a) �E 76 


A difference of color is actually expressed in the CIELAB 1976 system: 

* 2 * 2 * 2 

� E76 = �L 

+ �a 

+ �b 

with Lab coordinates 

Or 

* 2 * 2 * 2 

� E76 = �L 

+ �C 

+ �H 

with LCh coordinates. 

Where, for the difference between color 1 and color 2, with x=L * , a * , b * and C * : 

�x = x 2 � x 1 

�H * * 

= 2(C1 C2 

Warning: in the formula �H*� �h 

From this definition, CIE defines others �E to optimize the matching between visual 

and calculated perception. 

b) �E94 

One is the �E94, this formula will use for the tests to calculate color differences: 

�E94 = 

�L * � � 

� � 

� kLS L � 

2 

+ �C* � � 

� � 

� kCSC � 

With Si: ponderation factor 

2 

+ �H* � � 

� � 

� kH SH � 

2 

ki: correcting factor depending of observation conditions, typically ki=1 

X * = X2-X1 

The CIE recommendations are: 

SL = 1 

SC = 1+0,045C * 

SH = 1+0,015C * 

1 

* 

) 

2 sin( �h 

2 ) 


In practical, we consider that if: 

�E94 < 1, there are no perceptible differences for human eye 


1 < �E94 < 2, it is very difficult to see a difference, only a trained eye can detect 

something 

2 < �E94 < 4, the difference is perceptible for human eye 

�E 94> 4, we perceive a difference of color. 

�E76 and �E94 are the two mode of calculation available in Alwan’s software but 

there are other formulas. 

c) �E CMC (Color Mesurment Committee) 

�ECMC 

= 

If L*

1.2 Color Management 


1.2.1 Why do we need Color Management? 

In a graphic chain an original is scanned, displayed, proofed and printed with different 

devices at every stage of the process. All these devices have different behaviors: as 

we know every imaging device has its own colorimetric characteristics and is usually 

used with different system: RGB for displays, digital cameras and scanners and CMYK 

for proofers and printers. Moreover final file can be created in RGB, in CMYK or use 

several color spaces in the same document in some specific cases. (cf. figure 1-2) 

Figure 1-2: Color management proposes a way to connect all devices in a graphic chain 

through a common space 

We absolutely need to know how to manage the different characteristics of these 

because if we give the same data to two different devices, two printers for example, 

we will not print the same color because each device has its own interpretation of the 

data [2], working with multiple system components necessitates a way to get 

predictable, consistent color [3]. On the other hand, we need to have a common 

translator for colors, a system which is device-independent and which will be able to 

quantify and compensate for any device variability. This is the role of color 

management. 



1.2.2 Definition 

A color management system is defined as a set of methods that allows us to get 

consistent, predictable and reproducible colors. Side benefits of using a color 

management system can be the optimization of color separations and ink usage and 

the reduction of production costs. 

Every color management system is composed of several components: the reference 

color space called PCS ( Profile Connection Space), usually the LAB space, an input and 

an output space, an input profile, an output profile and a CMM (Color Management 

Module) which is the calculator. The tool that realizes these operations is described in 

ICC profiles. [4] (Cf. figure 1-3) 

INPUT 

SPACE 

INPUT PROFILE OUTPUT PROFILE 

CMM 

Figure 1-3: The architecture of a color management system 

1.3 ICC Profiles 

1.3.1 ICC organization 

Eight companies founded the ICC or International Color Consortium in 1993: Adobe, 

Agfa, Apple, Kodak, Taligent, Microsoft, Sun and Silicon Graphics. Since 1993 more 

than 70 companies joined them. The ICC objective is color exchange standardization. 

It is a regulator body that supervises color management protocols between software 

vendors, equipment manufacturers and users. They established specifications for color 

transformations between devices. These specifications are applied in a special file 

format called ICC profile. 

1.3.2 Definition and interest 

PCS 

CMM 

OUTPUT 

SPACE 

An ICC profile is a file that allows controlling color transformations. It interprets the 

different color pixels values, RGB for example and works out what color they actually 

refer to. The profile must accompany the image to allow a correct interpretation of 

the device dependent values. That is why every device must have a profile [5], [6], 

[7]. An ICC profile has a standard format and is neither vendor nor platform 

dependent. 



1.3.3 Rendering intent 

Generally, device gamuts are not large enough to reproduce all the colors we want. 

That is why ICC profiles provide four ways to still do the transformations. This 

paragraph will describe the different ways. [8] 

� Perceptual rendering intent (0): 

This mode preserves the relationship between colors but it deviates the original colors 

of the image. It allows having a good contrast in the image. This mode is well adapted 

for image or photography. 

� Relative colorimetric rendering intent (1) 

In this mode, only the values, which are outside of the destination gamut, are 

changed. The others values are preserved. 

� Absolute colorimetric rendering intent (1) 

This mode is almost the same as the relative mode except that in this case media 

(paper) color is taken into account 

� Saturation rendering intent (2) 

Maximum saturation is assigned to each value. This mode is not widely used. 

Figure 1-4: The four ICC rendering intents. On these examples the triangle represents 

input device gamut and the circle represents the output device gamut. 



1.3.4 Content of an ICC profile 

An ICC profile consists of two parts: the header and the tags (Cf. figure 1-5). 

The header contains information about the building of the profile: who, when, which 

color space, size of the file, the CMM used, type of device concerned, which table 

should be used. This is a standardized part which has a fixed size. 

The tags form the body of the profile. They refer to tables, which contain the data 

useful for the conversions. The size of tags varies depending on the type of device 

concerned by the profile (monitor, printer, proofer, scanner…) and the author of the 

profile. We can find two types of tags: required tags that are describing by ICC 

specifications for each type of profile [9] and are necessary to operate the profile. In 

addition you can also have optional tags that are not required to have a valid profile 

but are recognized by ICC too. 

Figure 1-5: The ISOwebcoated profile structure 

Each profile contains 6 tables: 3 from source space to PCS and 3 from PCS to 

destination space. 

We find a table for each rendering intent: perceptual, saturation and colorimetric. We 

can notice that absolute and relative colorimetric mode use the same table, the only 

difference is the use or not of the media color (white of the paper). [2], [10] 

TRICHON Amélie – PFE 2006/2007 16 

tags 

header


Figure 1-6: Structure of the tables in a profile. The information provided in the header 

indicates which table should be used (0, 1 or 2). 

The profile can be embedded in the image with all formats (eps, PDF, Tiff, jpeg, bmp, 

gif, png, pict) except the PS format because PS has its own color profiles. 

1.3.5 How to create an ICC Profile 

We need three steps to achieve accurate color called the “Three Cs”: calibration – 

characterization – conversion. [11] 

� Calibration: 

We should make sure that all adjustments of devices are compliant with established 

specifications. We should create defined and repeatable conditions: anything that can 

alter colors of the image must be identified and locked-down. 

� Characterization 

It is the creation of profile strictly speaking. We should now study the response of the 

output device depending on input values. We use a test chart: we send a reasonable 

sampling of color patches to the device and we measure the color we really obtain. 

The collected data can be used to create the corresponding tables of profiles 

� Conversion 

Source � PCS PCS � Destination 

Perceptual A2B0 B2A0 

Colorimetric A2B1 B2A1 

Saturation A2B2 B2A2 

This is the use of profiles that allows converting a file from one color space to 

another. 


1.4 ICC DeviceLink Profiles 


1.4.1 Definition 

An ICC DeviceLink profile, or DVLP, is a special kind of profile recognized by the ICC 

specification, which connects two profiles and their settings together. In a DVPL there 

is no PCS, the original and the final spaces are directly linked. So it provides a way to 

avoid the problems created when we convert 4-component data like CMYK in a 3component 

color space like LAB and then we reconvert in CMYK again. 

A DVLP is a unidirectional conversion without using a PCS. [12] 

1.4.2 Advantages 

A DVLP is often used for CMYK to CMYK data repurposing. It provides better control of 

special printability features of CMYK printing. By linking directly CMYK data, DVLPs 

provide a way to manage preservation of pure colors and to have a more accurate 

control on output GCR and TAC. 

Purity preservation is very useful for one-color-text or linework elements which remain 

easy and well registered. 

Improved GCR and TAC control help achieve better printability and possibly ink savings 

because on one hand black ink is cheaper than color inks and on the other hand DVLP 

allows the reduction of the quantity of ink on paper. 

Reminder: the TAC or Total Area Coverage is the maximal amount of overprinting ink 

that we can put on a media. In theory, maximum TAC is 400% but in practice, to have 

a better printability on the press, the maximum inking has to be limited to lower 

values. 

Some software used to create DVLPs allows you also to preserve secondary colors, 

achromatic colors (CMK, CYK, MYK) and 100% solid colors but these options are not 

provided in all software. 

By using a DVLP we reduce the number of operations: we need only one DVLP to 

convert a file from one space to another. With Device profiles two profiles are 

needed. 

1.4.3 Disadvantages 

As it was said before, a DVLP is a unidirectional transformation so if you want to step 

back the simplest way is to make a copy of the original file. 

Moreover, currently DVLPs are not supported by all RIPs and applications like Adobe 

Photoshop. For more information about DVLP please read [2], [10] and Alwan’s 

documentation available on their web site. 



1.5 Dynamic DeviceLink Profile (Dynamic DVLP) 

1.5.1 Defintition 

The main difference between “Static” and “Dynamic” DVLP is that Alwan Dynamic 

DVLPs are built on the fly after image or page analysis and take into account 

characteristics of: 

. source image/page color space, black generation and TAC 

- destination color space or device profile 

- software separation settings 

Dynamic DVLPs aim is to optimize color separations for a given file and desination 

process and to ensure improved printability, improved contrast, possibly reduction of 

costs, while maintaining color. 

1.5.2 Advantages and disadvantages 

The purpose of this study is also to find out the advantages as well as the advantages 

of Dynamic DVLPs. 

1.6 Black generation 

1.6.1 UCR and GCR 

A fundamental part of the color conversion process is the black printer generation. We 

can use several algorithms for that. The most common are called UCR (Under Color 

Removal) applied to neutral colors only and GCR (Gray Component Replacement) 

applied to all colors. The aim of the operation is to replace CMY inks by black ink to 

extend the gamut of colors achievable in dark areas, facilitate color control on the 

press and save ink. 

Often calculations are based on addition of density. There are many ways to calculate 

black generation but for GCR, we obtain always a monotonic increasing function (cf. 

figure 1-7) 

Figure 1-7: On this 

example, 50% of CMY inks 

are replaced by 30% of 

black ink. Next the color 

will be completed by a 

new percentage of CMY 

inks. 


Examples of equations you can find: 

a) Equation of Jonhson (1985) 

The total black z is given by the following formula [13]: 

6.1.1. 

6.1.2. 

c � a 

z = 

1 � a 

c � a 

� � 

� � 1� 

� � + ma(1� 

� � 

� K � 

a 

� � 

� � 

� � 

� � 

� K � 

) 

K 


With m: fraction of CMY color density replaced by equivalent density of black 

c: total density 

a: three-color density (CMY) 

K: convergent point of the additive Yule’s diagram 

b) Black printer model 2: 

The following equation includes a UCR concept too; the CMYK values after 

replacement are given by [14]: 

k ' = b min(c,m,y) 

c ' = c � uk ' 

m ' = m� uk ' 

y ' = y � uk ' 

With 0 � b � 1 and 0 � u � 1 

In this model, b and u represents respectively a black rate and a UCR rate and c,m,y, k 

the original dot area values. 

1.6.2 Alwan “Dynamic Maximum Black” generation 

Alwan “Dynamic Maximum Black” option aims to use more black (and less CMY) than 

what is possible with conventional GCR. Alwan “Dynamic Maximum Black” algorithms 

are not public, but the princple as explained to me is the following: 

“Dynamic Maximum Black” option consists of finding, for each color (that is to say 

each Lab combination) represented in the destination profile the C-M-Y-K output 

values which will have the optimal combination of max (K), min (CMY), min (�E), 


among all the possible solutions. 


This model seams to be more efficient for ink savings than conventional UCR/GCR but 

is not always very smooth depending on the output profile. 

This is the reason why Alwan strongly recommends the use of “Dynamic Maximum 

Black” option only with high quality ICC profiles and preferably, the actual printing 

process profile. 


2 PART TWO: EXPERIMENTAL STUDY 

2.1 Software used 


In this part you will find a description of the software developed by Alwan Color 

Expertise that I used for my study. You will find a summary of the applications and the 

main functions of the two software. For more complete information about software, 

please visit Alwan website or contact them. 

2.1.1 Alwan LinkProfiler 

a) Definition 

Alwan LinkProfiler is a profiling software that allows to create Static (Conventional) 

DVLPs between two CMYK spaces. It allows repurposing CMYK files prepared for one 

CMYK space to another CMYK space. For example, it allows changing paper or press 

before printing. [15] 

b) Features 

LinkProfiler includes features such as dot gain correction, TAC and black generation 

adjustments and preservation of purities. So no additional device profiles are 

necessary for different kind of papers and presses (cf. figure 2-1). 

c) Specific features used for the test 

Among all the available features of the software, here are the one used in the tests 

and their significance: 

- Separation tab: you will find GCR (1.7), a medium GCR and GCR(1.0), an Dynamic 

Maximum Black as described in section I.6.1 and I.6.2 for each case you can define the 

TAC you want to obtain, the “K start” value representing the lightness of the color for 

the CIE-L value where the black generation begins; the “K max” value representing the 

maximum amount of black in the output separation. 

- Purity tab: options “primary colors”, “secondary colors” and “100% solid colors were 

used. 

“Primary colors” means that an input pure color value (C, Y, M or K) will remain a pure 

color on the output, but the value can change: a 60% of cyan on the input can become 

63% of cyan on the output but there is no color contamination. 

“Secondary colors” is based on the same principle and applies to two pure colors. A 

mixture of yellow and magenta will remain a mixture of yellow and magenta on the 

output. 

“100% solid colors” means that input solid areas stay unchanged after the conversion 

(a 100% black text will stay 100% black text). So all these options are very practical to 

maintain and control printability on the press. 



To have an exhaustive description of LinkProfiler features, please refer to this report 

appendices or to Alwan documentation. 

d) LinkProfiler Interface: 



Figure 2-1: screenshots of LinkProfiler interface, arrows show the variable parameters in 

the tests 

e) Application of DeviceLink profiles 

LinkProfiler allows the creation of DVLPs but does not allow applying these profiles to 

a file. This is why it is necessary to combine LinkProfiler with a file processing 

software or a RIP. I choose another software from Alwan ColorHub framework : ICC 

Profile Processor. We access to ICC Profile Processor by lauching Alwan ColorHub and 

choosing “ICC Profile Processor” “Task” in the “Queue” settings tab (cf. figure 2.2). 

DVLPs created with LinkProfiler can be selected in “CMYK Color processing” settings, 

from “Default CMYK profile” pop-up menu (cf. figure 2-3). 


Figure 2-2: Alwan Color Hub interface 

Figure 2-3: ICC Profile Processor interface 



2.1.2 Alwan CMYK Optimizer 

a) Definition 


CMYK Optimizer is a color and separation Preflight and Optimization software. Like 

LinkProfiler, CMYK Optimizer can create CMYK to CMYK ICC DVLPs. Globally, this 

software has the same aim as LinkProfiler but it is more sophisticated. It includes a 

file handling and management framework (Alwan ColorHub), offers more features and 

options for a sophisticated color management like managing vectors and bitmaps 

separately etc… and the more important difference is its Dynamic TAC and color 

calculations. [16] 

b) Specific features 

The main difference between CMYK Optimizer and LinkProfiler DVLPs is the Dynamic 

nature of CMYK Optimizer DVLPs. Indeed, on the contrary of LinkProfiler DVLPs that, 

like all conventional DVLPs, apply the same CMYK to CMYK transformation to all input 

data, CMYK Optimizer does an analysis of the file to be processed and of the 

destination color space and profile before applying transformations. Depending on the 

settings of the Separation tab, DTAC (Dynamic TAC) tab and the original file 

separation, an optimal output TAC is calculated for each images or page file. DTAC 

parameters are the following: 

- “Filter image noise”: removes noise from images before TAC analysis. 

- “Tolerate excess TAC up to”: represents an area of high TAC that will be tolerated 

even if its TAC is superior to the target nominal TAC. Generally, this surface is small 

and corresponds usually to relatively small details. For example, if you print a portrait 

on a newspaper (TAC = 240), you can tolerate that part of the subject, such as the 

eyes pupils , have a TAC superior to 240. 

- “Keep nominal TAC up to”: defines the maximum area that can have the nominal 

TAC value. Beyond this surface, output TAC can be set to decrease below nominal TAC 

to avoid set-off or rippling problems. So if the file contains a very large dark area, the 

output TAC can be decreased accordingly. 

This technological advance allows adjusting and optimizing the transformation of the 

original file. 

The aim of the study is to determine if the dynamic effect has a real effect in practice 

or not. 


c) CMYK Optimizer Interface 

Figure 2-4: CMYK Optimizer Task interface 

Figure 2-5: CMYK Optimizer DTAC tab interface 



Figure 2-6: CMYK Optimizer Purity tab interface 

Figure 2-7: CMYK Optimizer Vector interface 

Figure 2-8: CMYK Optimizer Action tab interface 



2.1.3 Alwan ColorPursuit 

a) Definition 


ColorPursuit is software that allows comparing two files numerically without printing 

them. You can determine �E between an original file and a transformed file. This 

software will be used to evaluate color differences in files. [17] 

b) Comparison Process 

1.7. 

1.8. 

1.9. 

to open a widget and load an image to compare two images 

Figure 2-9: ColorPursuit interface 

ColorPursuit allows you to open a color or an image “widget, ie a window where you 

can choose a color or an image and compare it (�E) with another color or image. You 

can also build a workflow by linking widgets, assigning the relevant ICC Profiles and RI 

(Rendering Intents) to simulate the result of a succession of color transformation in a 

workflow. For this study, all transformations will be done using LinkProfiler (Static 

DVLPs) and CMYK Optimizer (Dynamic DVLPs) before comparing the results using 

ColorPursuit. ColorPursuit does not apply any color transformation to the files. 

Figure 2-10: In this example, the original file (n°1) is in ISOcoatedv2 color space and the 

transformed file (n°2) in ISOwebcoated color space. 

If the ICC profiles are embedded in the files they are automatically detected and used 

by ColorPursuit. If loaded files do not contain embedded profiles, you should choose 



the relevant profile in the profiles list to obtain the good softproof and calculations. 

Figure 2-11: Image Comparator window calculates �E difference between 2 images 

Image Comparator calculated average �E, maximum �E and the percentage of pixels 

having a �E less than the specified maximum �E. 

2.2 Tests and Works 

2.2.1 Description of the project 

a) Methodology 

The aim of this project is to study the interest of Dynamic DVLPs compared to Static 

(Conventional) DVLPs. This study will try to determine if the technological advance of 

Dynamic profiles is real or not. The project will look at the benefit of using each type 

of DVLPs for printers in terms of color matching, print contrast and ink savings. 

The project has been divided in three experimental parts: 



Part1 assessment of converted and optimized Tiff images using ColoPursuit calculations 

Part2 assessment of converted and optimized PDF documents using ColoPursuit calculations 

as well as visual assessment and SpectroEye measurements on produced proofs 

Each experimental part and measurement series involved up to 20 different color 

transformations chosen from plausible real life situations in a multinational prepress or 

press environment. Each color transformation has an associated number to facilitate 

its referencement in graphics. 

Applied color transformations are the following: 

1 iSOcoated_v2_ec i ISOwebcoate d 

2 iSOcoated_v2_ec i ISOnewspaper26V4 

3 iSOcoated_v2_ec i SWOP2006_Coated5v2 

4 iSOcoated_v2_ec i Japan Color 2001 Coate d 

5 ISOwebcoate d ISOnewspaper26V4 

6 ISOwebcoate d SWOP2006_Coated5v2 

7 ISOwebcoate d Japan Color 2001 Coate d 

8 ISOwebcoate d iSOcoated_v2_ec i 

9 ISOnewspaper26V4 ISOwebcoate d 

10 ISOnewspaper26V4 SWOP2006_Coated5v2 

11 ISOnewspaper26V4 Japan Color 2001 Coate d 

12 ISOnewspaper26V4 iSOcoated_v2_ec i 

13 SWOP2006_Coated 5 v 2 ISOwebcoate d 

14 SWOP2006_Coated 5 v 2 ISOnewspaper26V4 

15 SWOP2006_Coated 5 v 2 Japan Color 2001 Coate d 

16 SWOP2006_Coated 5 v 2 iSOcoated_v2_ec i 

17 Japan Color 2001 Coate d ISOwebcoate d 

18 Japan Color 2001 Coate d ISOnewspaper26V4 

19 Japan Color 2001 Coate d SWOP2006_Coated5v2 

20 Japan Color 2001 Coate d iSOcoated_v2_ec i 

Figure 2-12: Twenty color transformations were used to assess probable situations 

b) Criteria of comparison 

i. Colorimetric accuracy assessment 



To evaluate these transformations color accuracy, converted files were compared with 

their corresponding originals using: 

Part 1: color managed images are assessed using ColorPursuit software as described in 

section 2.1.3. 

Part 2: color managed PDFs are assessed in 2 ways. 1- using ColorPursuit. 2- by 

producing proofs that were measured with GMB Spectroeye. 

ii. Ink savings calculations 

All Ink consumption and savings were done using CMYK Optimizer Ink Statistics 

manager intrerface and data base. 

To evaluate Dynamic profiles ink savings, ink statistics reports generated by CMYK 

Optimizer have been used (cf. figure 2-14). When “Ink Consumption Statistics” is 

chosen in CMYK Optimizer “action” tab (cf. figure 2-8), ink statistics including 

individual CMYK inks consumption before and after optimization are generated for 

each processed file. 

To evaluate conventional DVLPs ink savings, files that were processed using 

LinkProfiler DVLPs and ICC Profile Processor were analyzed using CMYK Optimizer 

“Check Only (Preflight)” mode.(Figure 2-13) This option allows the preflighting of a 

file without applying any transformation to it. Corresponding preflighting report and 

log contain color and separation information about the file including its ink 

characteristics and consumption. 

Figure 2-13: CMYK Optimizer “Check Only (Preflight)” action 



Figure 2.9 Extract of a report. Ink amounts and consumption statistics are in bold 

2.2.2 Part 1: Processing of Tiff images 

a) Test files 

Two types of images were used: GMB TC 3.5 chart including 432 patches was used for 

colorimetric measurements. Parts of Ugra VPR (Visual Print Reference) were selected 

and used for measurement and visual assessments (cf. figure 2-14). 

Figure 2-14: test files 


) Test workflow processing 


Figure 2-15: Schematic diagram of image files processing workflow 

c) Settings 

For this study, four series of tests with different separation options were performed: 

Series 1 Medium GCR 

Series 2 Heavy GCR 

Series 3 Maximum GCR 

Series 4 

Dynamic Maximum Black (Alwan intelligent Black 

generation) 



Aim of settings 1 & 2 was to study the comparative effect of different levels of 

conventional GCR on the color accuracy for each type of DVLP transformation, Static 

and Dynamic. 

Aim of series 3 and 4 was to study the comparative effect of Dynamic Maximum Black 

generation on ink savings for each type of DVLP transformation, Static and Dynamic. 

A maximum of 20 different Transformations were used for the test series as listed in 

figure 2-12. A complete description of the used settings can be found in Appendix 

tables 1 to 5d, p.3 to 15. 

d) Results and interpretation 

i. Colorimetric accuracy assessment 

File Measurements: 

For each converted file, ColorPursuit was used to calculate the �E94 difference 

between original and converted CMYK files. The aim of the measurements is to 

determine which transformation (Static or Dynamic) gives more color accurate results. 

ColorPursuit software calculates and displays average �E94, maximum �E94 as well as 

the percentage (in number) of pixels that have a �E94 which is lower than the 

specified �E limit. In this analysis �E limit has been set to 4 because it seems to 

correspond to an accepted average �E by the industry as well as by Standards (ISO 

12647-2/7). 

Results of series 1 (Medium GCR): 

Terminology: 

In this report, the following terms found on the graphs refer to the corresponding ICC 

profile/color space: 

Coated = ISOcoated v2 

• Web = ISOwebcoated 

• SWOP = swop 2006 coated 5v 

• News = ISOnewspaper 26v2 

• Japan = Japan color 2001 coated 

• RGB = Adobe RGB 1998 


VPR (Visual Print Reference) images analysis: 


Globally, we can say that a trend emerges: for 11 cases among 20, dynamic 

transformations give a better (lower) �E than the static/conventional transformation. 

�E average is lower by 1.1 to 2.6, �Emax is lower by 2.0 to 4.1 units and number of in 

gamut colors (having an individual color shift < �E 4) is 3.8% to 50.8% higher with the 

dynamic technology for these images, depending on the color transformation. 

This result is most probably due to the content dependant nature of dynamic 

transformations which convert colors and adapt TAC taking into account differences 

between strongly inked images and weekly inked images, and differences between 

input and output color gamuts capabilities. 

Figure 2-16a: Average �E on VPR 

Figure 2-17b: Maximum �E on VPR with medium GCR 



Figure 2-18c: % of output colors within �E 4 on VPR images 

The same observations can be done for Heavy GCR color separation. For complete 

results, please refer to Appendix. 

• TC 3.5 chart conversions analysis: 

There is hardly any difference between the two methods except for one case, Coated 

to News (2), where the dynamic transformation of the chart gives a slightly better 

result (�E average lower by 1.2) than conventional transformation, cf. figure 2-19. 

This result which is contradictory with the images assessment result was investigated. 

Two possible reasons can explain this result. The first reason would be that TC 3.5 

chart contains 400% TAC patches which inhibits in some cases input Dynamic TAC 

according to Alwan, the second being that the chart patches are too small to activate 

output Dynamic TAC which is surface dependant. 

With both dynamic processes deactivated, similar and even identical results would be 

expected from the two systems. 

In order to test the threshold of Dynamic processing, Nominal TAC area (see DTAC tab 

figure 1-5 page 27) was decreased until a difference was measured between Static and 

Dynamic processing. A difference was noticed for a setting of 1cm2 which indeed 

corresponds approximately to the TC 3.5 patches surface. So it seems that the 

assumption was probably true: the parameter ”Keep nominal TAC up to” is behind the 



fact that in TC 3.5 chart tests, we did not see the “Dynamic” effect of CMYK 

Optimizer. 

This seems to confirm that the surface of 25 cm2 was not adapted for the size of TC 

3.5 patches but is probably adapted for more conventional files received by printers 

everyday. This may be confirmed by the following tests done on PDF test forms (para 

2.2.3). 

Figure 2-19: Average �E for TC 3.5 chart with medium GCR 

Conclusions of color matching assessment of images: 

VPR images: For VPR images, it has been possible to clearly establish that Dynamic 

DVLPs achieve better color accuracy than conventional DVLPs. 

Average and maximum �E as well as gamut mapping of out of gamut colors were in 

favor of CMYK Optimizer Dynamic technology. 

TC 3.5: The conclusion regarding TC 3.5 chart is more reserved as there is hardly any 

colorimetric difference between the two systems except for severe gamut mapping 

situations like for Newspaper output. 


ii. Ink Savings: 

Measurements: 


As described in section 2.2.1.a.ii), CMYK Optimizer reports ink consumption of each 

image file including cyan, magenta, yellow and black inks separately. It compares 

original and converted files ink consumption and determines three values: 

�K = K final -K initial where K i is the percentage of black plate ink coverage. 

�CMY = CMY final -CMY initial where CMY i is the sum of percentage of cyan, yellow and 

magenta plates ink coverage. 

�CMYK = CMYK final -CMYK initial where CMYK i is the sum of percentage of cyan, yellow, 

magenta and black plates ink coverage. 

Results for TC 3.5 chart analysis: 

TC 3.5 chart output TAC and ink savings assessment confirms in a way the colorimetric 

assessments. There is no clear difference between the two methods except for the 

transformations 2, 5, 14 and 18 (ISOnewspaper26v4 destination) which are un favor of 

Dynamic DVLPs. For newspaper output, TAC reduction and ink savings are higher by 7% 

and 3% respectively with Dynamic DVLPs. We may explain this small difference by 

noting that DTAC parameter applied to dynamic transformations is not effective on a 

test chart having 400% values and relatively small areas for dark colors, hence we 

cannot see the full dynamic effect on such a file. If we want to increase the effect of 

DTAC, we must choose a “Keep nominal TAC up to” value which is under 1 cm2. 

Despite this image specific limitation, we can observe that CMYK Optimizer achieves 

higher ink savings with a small gamut output profile, than do conventional DVLPs. 

However, we observe that the decrease of inking on the studied surface is not 

systematic. 



Figure 2-20: �CMYK on TC 3.5 with the GCR (2.4) settings (series 1) 

These results in terms of color accuracy and ink savings have been found to be 

practically identical with all levels of GCR: Heavy GCR, Maximum GCR and Dynamic 

Maximum Black. 

2.2.3 Part two: test on PDF 

a) Testing file 

A PDF/X-3 2001 A3 test form has been created using “Adobe InDesign” software. 

The document is composed of two pages with parts for numerical evaluation (charts, 

control strip) and parts for visual evaluation (vignettes, images) as shown on figure 

2.17. 

To simulate a real life situation where multi-page PDF documents contain images and 

pages produced with different color profiles and separation options, we decided to 

separate each set of images with a different color profile. 

The test document hence contains 4 sets of 3 VPR images converted from their source 

RGB color space to a predefined output CMYK color space, and a car magazine cover 

page having an unknown color profile and separation characteristics. 

All PDF content characteristics and color management parameters can be found in the 

Appendix. 



Figure 2-21: PDF test form created for the tests (left: page1; right: page2) 

Figure 2-22: PDF test form elements 



We know that Static DVLPs loaded in a RIP will always apply the same color conversion 

regardless of the content of the input files. 

Alwan Dynamic technology applies context dependant color conversion to input files in 

order to generate Dynamic DVLPs on the fly prior to color conversion. 

The context parameters are: 

- input image/page color space, TAC and GCR 

- chosen output color space, TAC and GCR 

- software custom settings 

This will be tested in this study as our document includes elements of different color 

spaces and separation characteristics as shown in figure 2.12. The first column of parts 

1, 2, 3, 4 and parts 5, 7 and 8 will be used for numerical evaluation and the second 

and third columns of parts 1, 2, 3, 4 and part 6 will be used for a visual evaluation. 

(Cf. figure 2-22) 

b) Process 

Figure 2-23: PDF testform processing workflow 


c) Settings 


Two modes of colorimetric transformations were tested in this part: the first called 

“Color Matching” with parameters of series 2 and the second called “Dynamic Black 

generation” - with the parameters of series 4 as described in part 2.2.The complete 

parameters used for applying DVLPs are in the Appendix in Tables 6 and 7. 

The aim of color matching settings is to achive maximum color accuracy hence 

minimum �E. The aim of “Dynamic Maximum Black” settings is to save maximum ink 

on the press. 

The original PDF contains 3 different CMYK color spaces and an undefined color space. 

The output color space for the color transformations has been arbitrarily chosen to be 

Fogra39/ISOcoatedv2. 

So for the two sets of transformations, numbers 1 to 3 refer to conventional/Static 

transformations and numbers 4 to 6 refer to Dynamic transformations. 

Once color managed and optimized, the files were proofed on an Epson SP 4000 inkjet 

digital printer, with EPSON Ultra Chrome inks, semi-mat contract proofing paper and 

Fogra39/ISOcoatedv2 simulation. The inkjet printer has been calibrated and 

characterized before proofing. Proofs were controlled and validated using Ugra/Fogra 

media wedge and ISO 12647-2 tolerances and then used for measurements and visual 

evaluations. 

d) Measurements 

To evaluate the performance of each type of DVLPs, two different methods were used 

to assess the achieved color accuracy of each one: 

1- calculations done using ColorPursuit. 

2- measurements using two spectrophotometers: X-Rite EyeOne/iO and Xrite 968 

Evaluation of ink savings was done using Alwan ColorHub Ink Statistics Manager. 

e) Results and interpretation of color matching 

i. Colorimetric accuracy: 

• Color Pursuit: test on files 

The same methodology that was used to test TIFF images in part 1 of this study, was 

used for PDF test files. 

However, for PDF files the results differed significantly from single images tests (cf. 

figure 2-24 and 2-25). 

Indeed we can see from the results shown below that CMYK Optimizer does take into 



consideration the input colour space and separation of the different elements of the 

PDF page and adapts its optimization accordingly. 

Figure 2-24: Medium �E 94 on car image from PDF test form with “color matching” 

settings 

Figure 2-25: Medium �E 94 on car image from PDF test form with “Dynamic Maximum 

Black” settings. 

Moreover our other evaluation criteria confirmed this trend. Maximum �E 94 and 

percentage of pixels which have �E 94

• Eye One Io: proof measurement 


The Eye One Io measures automatically the colorimetric values (CIELAB) of each patch 

on a chart and gives the results in a text file. It was used on TC 3.5 chart and 

Medienkeil control strip. Then, GMB Mesure tool 5.0 software was used to compare 

the measurements obtained from optimized files and those obtained from the original. 

The software gives the average �E and the associated standard deviation for the total 

chart, the best 90% patches and the worst 10%. It gives the maximum �E on the total 

chart and the best 90% patches too. 

The results show in each case that Dynamic DVLPs �E is always lower than Static DVLP 

as you can see on figures 2-26 to 2-29. 

DeltaE 94 

Figure 2-26: Medium �E 94 on TC 3.5 from PDF test form with “color matching” settings 

DeltaE 94 

Figure 2-27: Medium �E 94 on Medienkeil from PDF test form with “color matching” 

settings 

Medium DeltaE 94 on TC 3.5 from PDF test form 

7 

6 

5 

4 

3 

2 

1 

0 

8 

6 

4 

2 

0 

total best 90% worst 10% 

Transformations with color matching 

settings 

Medium DeltaE 94 on Medienkeil chart from PDF 

test form 


Transformations with color matching 

settings 

static 

dynamic 

static 

dynamic 



Figure 2-28: Medium �E 94 on TC 3.5 from PDF test form with “Dynamic Maximum Black” 

settings 

DeltaE 94 

DeltaE 94 

8 

7 

6 

5 

4 

3 

2 

1 

0 

Medium DeltaE 94 on TC 3.5 from PDF test 

form 

7 

6 

5 

4 

3 

2 

1 

0 


transformations with Dynamic 

Maximum Black settings 

Medium DeltaE 94 on Medienkeil chart 

from PDF test form 


transformations with Dynamic 

Maximum Black settings 

Figure 2-29: Medium �E 94 on Medienkeil from PDF test form with “Dynamic Maximum 

Black” settings 

For more complete results, please refer to Fig. 10, 11, 16 and 17 in appendix. 

• Manual spectrophotometer: proof measurement 

To confirm the results obtained from the charts, CIELAB values were collected from 

the printed images of the first column of PDF test form and from the graduated areas 

of the page. 

TRICHON Amélie – PFE 2006/2007 46 

static 

dynamic 

static 

dynamic


The tested points are the following: five in the image (green, orange, yellow, red and 

blue) and three in each graduated strip (left, middle and right the upper part of the 

strip) 

1 

2 

Figure 2-30: Positions of the test points on images from page 1 of PDF test form 

For each part of the page, the average has been done and reported on a graph with 

the following notation: 

RGB: images from line 1 

3 

5 

ISOcoated: images from line 2 

SWOP: images from line 3 

JAPAN: images from line 4 

Graduated: graduated stripes 

4 

6 7 8 9 10 11 

The following figure gives medium �E between original proof and optimized 

reproductions for the two types of settings (cf. figures 2-31 and 2-32) 



Figure 2-31: Medium �E on images from page1 of PDF test form with “color matching” 

settings 

Delta E 

Delta E 

20.0 

16.0 

12.0 

8.0 

4.0 

0.0 

Figure 2-32: Medium �E on images from page1 of PDF test form with “Dynamic Maximum 

Black” settings 

Medium delta E for color matching settings 

on PDF test form 

RGB ISOcoated Swop Japan Graduated 

images of page 1 of the PDF test form 

20.0 

16.0 

12.0 

8.0 

4.0 

0.0 

static 

Medium delta E for Dynamic Maximum 

Black settings on PDF test form 

dynamic 

RGB ISOcoated Swop Japan Graduated 

static 

images of page 1 of the PDF test form dynamic 

We can note that there is no significant difference of colorimetric accuracy when the 

original file is in RGB but for all the other cases, the advantage of Dynamic DVLPs is 

clear even if the right profile is used with Link Profiler like in case B (ISOcoated input). 

For more complete results, please refer to Fig. 12 and Fig. 18 in appendix. 


ii. Ink savings: 


We notice that in all cases, Dynamic DVLPs give a lower level of inking than Static 

DVLP (cf. figures 2-33 to 2-36) 

Moreover, we can see that “Colour Matching” settings do not save ink, but this is 

understandable because it is not the first aim of this setting. 

With “Dynamic Maximum black” we can see that the differences of inking level are 

significant between Static and Dynamic processing and, as seen in the last paragraph, 

saving more ink does not produce any additional colorimetric distortion. 

% of ink 

20 

15 

10 

5 

0 

Delta CMYK total on page 1 of PDF test 


ISOcoated output SWOP output JAPAN output 

Transformations with color 

matching settings 

static 

dynamic 

Figure 2-33: Results of ink saving test on PDF test form for “color matching” settings 

Figure 2-34: Results of ink saving test on PDF test form for “color matching” settings 



Figure 2-35: Results of ink saving test on PDF test form for “Dynamic Maximum black” 

settings 

% of ink 

10 

5 

0 

-5 

-10 

Delta CMYK total on page 2 of PDF test 


ISOcoated output SWOP output JAPAN output 

Transformations with Dynamic 

Maxim um Black settings 

static 

dynamic 

Figure 2-36: Results of ink saving test on PDF test form for “Dynamic Maximum black” 

settings 

If we observe one image in particular, for example, the image of the car - having an 

undefined CMYK colour space - we can see that not only we save more ink with the 

dynamic processing but, as found previously (cf. figure 2-24 and 2-25) we were able to 

obtain better colorimetric accuracy as well. 

Moreover, this conclusion was confirmed by the visual evaluations (cf. next part iii) 



Figure 2-37: Results of ink saving test on car image from PDF test form for “color 

matching” settings 

Figure 2-38: Results of ink saving test on car image from PDF test form for “Dynamic 

Maximum Black” settings 

For more complete results, please refer to Fig. 13, 14, 19 and 20 in appendix. 

iii. Visual evaluation: 

A sample group of seven persons were asked to observe the proofs, compare Static 

optimization with Dynamic optimization and say if they see a difference. If they did, 

they had to say which proof is closest to the original proof. 

In a large majority of proofs (7/10), observers did see a difference between the two 

processing. For only 1 proof, some observers found that the Static DVLP proof was 

more faithful to the original than the Dynamic DVLP proof. 

The observers assessments can be summarized as follows: 



. 3 out of 4 observers said they saw a difference between proofs produced with 

LinkProfiler and those produced with CMYK Optimizer files 

. 3 out of 4 of those who saw a difference said that CMYK Optimizer proofs were closer 

to the original reference proof than LinkProfiler proofs. 

This leads us to say that a minority of observers found that either Static or Dynamic 

DVLPs proofs matched equally the reference, and a majority of observers found that 

Dynamic DVLP gave a better visual result. 

Since we found previously that Dynamic processing ensures more ink savings, visual 

evaluation can be considered as a confirmation of the Dynamic technology advantage 

as ink savings do not seem to lead to visual or colour distortions. 

However, we should be careful with these results because visual observations are 

subjective. 

For more complete results, please refer to Tab. 8 and 9 in appendix. 


3 CONCLUSION 


Conventional “Static” DVLPs and Content Dependant "Dynamic" DVLPs allow 

management and standardization of colour in a print oriented workflow. 

This project aim was to study the performance and possible advantages of these two 

DVLP types using a numerical/objective method as well as a visual/subjective method. 

Color Matching and Ink savings were retained for numerical assessment; visual 

matching with original reference was retained for visual assessment. 

The conclusion of this study is that Dynamic DVLPs give better results than Static 

DVLPs generated by conventional DVLP builders and applied with color management or 

a RIP software. 

Numerically, Dynamic DVLPs provided: 

� better color accuracy for the 2 tested settings : Color Matching and Ink Savings 

� With “Color Matching” settings: better color accuracy was achieved with lower levels 

of ink usage on the press 

� With "Ink Savings" settings: differences in ink usage were significant between Static 

and Dynamic DVLPs processing. More ink savings were achieved using Alwan CMYK 

Optimizer “Dynamic Maximum Black” option without introducing additional colour 

deviations. 

A very illustrative example was the "Car" image where we saw more ink saved with 

Dynamic DVLPs while maintaining a better colorimetric accuracy (cf. figure 2.20 and 

2.21.) 

Visual evaluations seemed to be confirm this trend: 

. 3 out of 4 observers said they saw a difference between proofs produced 

with LinkProfiler and those produced with CMYK Optimizer files. 

. 3 out of 4 of those who saw a difference said that CMYK Optimizer proofs 

were closer to the original reference proof than LinkProfiler proofs. 


In conclusion we can say that Dynamic DVLPs allow printers to 


repurpose their client files in order to adapt them to their printing 

process and/or to save ink with much superior results in terms of 

color accuracy, print quality and ink savings than what is possible 

to achieve with conventional Static DVLPs. 


4 BIBLIOGRAPHY 


[1] Gaurav Sharma. Digital Color Imaging handbook, color fundamentals for digital 

imaging. CRC press, chapter one, pp. 34-40. 

[2] Abhay Sharma. Understanding Color Management. Thomson Delmar learning, 2004. 

2nd printing April 1998. 

[3] Richard M.Adams II, Bruce G.Mills and Christy M.Hubbard. Color Management. 

GATFWorld, 1994, volume 6 issue 3, pp.33-42. 

[4] Ben Starr. DeviceLinkProfiles/Repurposing CMYK. Progressive Color Media LLC. 

18/01/05. 

[5] Richard M.Adams II and Joshua B.Weisberg. The GATF practical guide to color 

management. GATFPress . Pittsburg, 1998. 

[6] International Color Consortium. The role of ICC profiles in a colour reproduction 

system. [In line] available on 

consulting 

12/02/07 

[7] David McDowell. What is a color management system? GATFWorld, july/August 

2000, volume 12 n°4, p.5. 

[8] Lionel Chagas. La gestion de la couleur. [in line] available on 

consulting 

12/02/07 

[9] International Color Consortium Specification ICC 1:2004-10. [In line] available on 

consulting 15/02/07. 

[10] Edward J.Giorgianni and Thomas E.Madden. Digital Color Management. Addision 

Wesley. 

[11] Dawn wallner. Building ICC profiles – the Mechanics and Engineering [in line] 

available on consulting 13/02/07. 

[12] Dimitris Ploumidis. DeviceLink Profiling. 

[13] Tony Johnson. Colour management in graphic arts and publishing. Pira technology 

series, 1996, pp.31-32. 

[14] Iino and Berns. Building Color Management Modues Using linear optimization. 

Journal of Imaging Science and Technology, vol.42, March/April 1998, p.106. 



[15] Alwan Color Expertise. Alwan LinkProfiler Manual, Spot on proofs and press to 

press match. [In line] available on http://www.alwancolor.com consulting 10/02/07 

[16] Alwan Color Expertise. Alwan CMYK Optimizer server v2.7 Manual. [In line] 

available on http://www.alwancolor.com consulting 10/02/07 

[17] Alwan Color Expertise. Color Pursuit [In line] available on 

http://www.alwancolor.com consulting 10/02/07 


5 GLOSSARY 

RGB: red-green-blue 

CMYK: cyan-magenta-yellow-black 

RIP: raster image processor 

DVLP: DeviceLink Profile 

TAC: Total Area Coverage 


CMYKex: CMYK exchange, appoints the spaces between original files and CMYK files 

CMYKpr: CMYK printer, appoints the final spaces after applying DVLP. 


6 ACKNOWLEDGMENTS 


I would like to thank Dr. Lionel Chagas, researcher and teacher at EFPG for his advices 

and help during the entire project. 

I thank a lot Mr. François Fournié and Alwan Color Expertise team for the time, advice 

and help they gave me. 

I thank Mr. Elie Khoury for giving me all the means to succeed in this project. 


KEY WORDS 

ICC profile 

Static (Conventional) DeviceLink profile 

Dynamic DeviceLink profile 

Colorimetric accuracy 

Ink savings 

CMYK Optimizer 

LinkProfiler

Use of DeviceLink Profiles for graphic industries ... - Impressed

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