Gamut Mapping and Digital Color Management EHINC 2005 EHINC 2005, Lille 1
Overview Digital color management Color management functionalities Calibration Characterization Using color transforms Quality control ICC Profile quality issues 2
Gamut Mapping and Digital Color Management Digital color management 3
Digital color management Why color management? Different types of color devices: input - monitor - output 4
Digital color management The color management solution TRANSFORM TRANSFORM TRANSFORM 5
Gamut Mapping and Digital Color Management Color management functionalities 6
Color management functionalities Calibration Characterization Device modeling Gamut mapping Building color tables Using color transforms Quality control 7
Calibration Checking and readjusting color devices to predefined settings and specifications according to manufacturer s recommendations. Calibration = Measurement + Control 8
Calibrating color devices Input Monitor Output Automatic internal scanner calibration 9
Calibration Defining tonal responses Condition on single ink processes Quantity is visual Tonal response is linear Fix endpoints of the ink processes Ink limitations for C, M, Y and K Multi-density inks Increase apparent resolution Measure ink combinations Choose an Ink Mixing Path 10
Defining tonal responses Measured quantities are visual Tonal response is linear Visually stable Smooth color gradations Multi-density inks: dot gain? Equal ink steps are equal visual steps => better use of levels in profiling => uniform characterization target Use CIELAB L* for C, M and K C* for Y 11
Defining tonal responses Fix endpoints of the ink processes 100 C* (for yellow) Differs! Fixed in L *or C* 90 80 70 60 50 40 30 20 NOT in Ink % 10 0 0 10 20 30 40 50 60 70 80 90 100 Ink % 12
Defining tonal responses Ink limitations What is needed? What is possible? Large enough gamut Avoid bleeding, coalescence Drying characteristics Difficult trade-off Must be done by end user Determination of bleeding & gamut based on measurements 13
Defining tonal responses Ink limitations (cont d) Bleeding: Correlate characteristics 14
Defining tonal responses Ink limitations (cont d) Ink limitation wizard: Trade-off: bleeding <=> gamut 15
Multi-density inks Increase apparent resolution (Cl,Ch,Ml,Mh,Y,K) More complex 0% Multi-density inks: More opportunities Practical solution: Ink Mixing Balance: visibility dots and ink use 0% Light cyan 100% Heavy cyan 100% 16
Multi-density inks Measure ink combinations Lightness Variations Hue variations L* ht g i L b* % 100 n cya 0% 100 y v a e 90 80 % 100 70 H n cya 60 50 0% 0 40 2 0 4 2 6 4 8 6 8 10 10 a* 12 12 14 14 16 16 17
Multi-density inks Choose an Ink Mixing Path 90 Light C Ink % CL and CH 80 70 60 50 40 Light 30 Heavy 20 Heavy C 10 0 0 10 20 30 40 50 60 70 80 90 Ink % global C 100 18
Characterization Measuring the color behavior of a color device. During characterization a relationship is built between the colors produced by a device and a device independent color space Building color transforms => Profile Device dependent space device independent space 19
Characterization Input devices Scanners Digital cameras Transparent (IT8.7/1) or Reflective (IT8.7/2) photographic scanner target (Agfa/Fuji/Kodak) Internal image rendering, often to srgb or wide gamut RGB space RIMM / ROMM spaces 20
Characterization Scanners Scan Digital Reference Data Color measurements Calculate Profile 21
Characterization Monitors Measure Calculate Profile Test pattern 22
Characterization Output devices Measure Print Digital Reference Data Calculate Profile IS12642 printer target 23
Building color transforms Device modeling Gamut mapping Building color tables CMS 24
Device modeling : Scanners Device dependent color space: linear RGB space R = I ( λ ) R ( λ ) S R ( λ ) dλ G = I ( λ ) R ( λ ) S G ( λ ) dλ B = I ( λ ) R ( λ ) S B ( λ ) dλ with I(λ) the illuminant in the scanner R(λ) the reflectance curve of the scanned object SR(λ) the spectral sensitivity of the red scanner channel 25
Device modeling : Scanners Scanner model Relation RGB and XYZ not unique due to scanner metamerism Empirical technique for scanner modeling based on polynomials e.g. X = arr + agg + abb + argrg + agbgb + abrbr + arrr2 + aggg2 + abbb2 + Y = brr + bgg + bbb + brgrg + bgbgb + bbrbr + brrr2 + bggg2 + bbbb2 + Z = crr + cgg + cbb + crgrg + cgbgb + cbrbr + crrr2 + cggg2 + cbbb2 + 26
Device modeling : Monitors Device dependent color space: RGB space Modeling Simple model : Gain Offset Gamma model (GOG) based on chromaticity coordinates of the phosphors gamma values for the phosphors white point black point works fine if phosphors are stimulated independently Complex model : Polynomial fitting 27
Device modeling : Monitors GOG model Luminance γr R R = ar d + br 255 γg G G = ag d + bg 255 γb B B = ab d + bb 255 X m11 Y = m21 Z m 31 m12 m22 m32 m13 R m23. G m33 B γ >1 Voltage with (Rd,Gd,Bd) the monitor RGB values (R,G,B) the tristimulus values relating to the phosphors (X,Y,Z) the XYZ tristimulus values The mij values are determined by the phosphor chromaticity values and the white point of te display 28
Device modeling : Printers Device dependent color space: CMY,CMYK,RGB,... Printer model Function colorant space color space Domain colorant cube Range gamut 29
Device modeling : Printers Modeling Subtractive color mixing Lambert-Beer law e.g. photography Printing devices with halftoning Neugebauer process combination of additive and subtractive color mixing e.g. offset printing 30
Device modeling : Printers Neugebauer model e.g. X of the XYZ space for a CMY process: X = 1 c 1 m 1 y X w + c 1 m 1 y X c + 1 c m 1 y X m + 1 c 1 m yx y + cm 1 y Xcm + c 1 m yx cy + 1 c myx my + cmyx k with c,m,y, the CMY colorant values More complex behavior Polynomial fitting 31
Gamut representations xy-chromaticity diagram provides limited gamut information incorrect for scanners sufficient for monitor poor gamut info for printers Needed color values primary and secondary colors 32
Gamut representations Nrofcolors per lightness / hue section Pro: limited data to represent a gamut more accurately good gamut representation if both systems have a similar color mixing behavior e.g. comparison of different screening techniques gamut due to different total amount of ink Contra: insufficient information to compare gamuts of different types of color reproduction devices accurately 33
Gamut representations Offset process with different total ink amounts Nrofcolors per lightness section 10000 Offset 400 Nrofcolors 8000 Offset 250 6000 Offset 200 4000 Offset 150 Offset 100 2000 Offset 50 0 0 20 40 60 80 100 Lightness 34
Gamut representations Offset process with different total ink amounts Nrofcolors Nrofcolors per hue section 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Offset 400 Offset 250 Offset 200 Offset 150 Offset 100 Offset 50 0 60 120 180 240 300 360 Hue 35
Gamut representations Gamut cross sections Most important cross sections constant lightness constant hue Different approaches Heuristic techniques Analytical techniques: measuring many printed color patches detection of boundaries; e.g. with convex hull construction of a model for the color device calculation of gamut boundaries based on this model Required for accurate gamut mapping 36
Gamut representations Gamut cross sections Offset processs / srgb L* = 50 Offset process / srgb hue = 0o 37
Gamut representations Offset processs lightness intersection at primary and secondary colors Red Yellow Green Cyan Blue Magenta 38
Gamut mapping Different gamuts due to different color technology or device settings Input device Output device Proofing device 39
Gamut mapping Rendering intents = mapping strategies Photographic (perceptual) Colorimetric (absolute/relative) Computer graphics (saturation match) Proofing / Simulation Source profile Destination profile Proofer profile 40
Gamut mapping Colorimetric mapping Perceptual mapping 100 100 L* L* 50 50 Original gamut 0 C* 0 Reproduction gamut 0 20 40 C* 60 80 100 Gamut mapping 41
Building color tables Regular grid in device dependent space or device independent space Number of sampling values Sampling points Interpolation Multi-linear interpolation Tetrahedral interpolation 42
Using color transforms Color Management Module (CMM) in OS => to be used by applications TRANSFORM TRANSFORM CMS TRANSFORM 43
Using color transforms Link keys Rendering intent Black point compensation Smart CMM Link exceptions Black: to render text 400 % CMYK Keep white Primary and secondary colors 44
Using color transforms Link key: Black point compensation L* L* BPC C* C* 45
Using color transforms Link key: Smart CMM Errors mainly at gamut boundary L* Interpolation error L*-gamut cross section Proofing device b* a* Gamut proofing device Gamut mapping Offset 46
Quality control Goal Monitor consistency of print Identify problems Propose solutions (e.g. calibration) Method Measure control strip 47
Quality control Result: Consistency scores Global and per color Linked to tolerances 48
Quality control Solution: Rule based system Based on expertise & common sense 49
Quality Management System QMS Quality control of the workflow 50
Gamut Mapping and Digital Color Management ICC: an open CMS 51
ICC International Color Consortium Goal: Create, promote and encourage the standardization and evolution of an open vendor-neutral, cross-platform color management system architecture and components 52
ICC: Profile format Concept : Color processing model Converting device color data in and out of a reference Profile Connection Space (PCS) Combination of 3x3 matrix, 1D and multidimensional LUTS Smart profiles, dumb CMM Structure Profile structure : Header Tag description Tags 53
ICC: Profile Classes Profile classes Input device Display device Output device Color space (device to color space) Device link (device1 to device2) Abstract profile (PCS to PCS) Named color (e.g. Pantones) 54
ICC: Color spaces Different spaces are supported XYZ, CIELAB (PCS) Gray RGB, HLS, HSV CMYK, CMY Profile Connection Space based on CIE 1931 Standard Observer D50 illuminant, 500 lux 0/45 or 45/0 Measurement Geometry Black and white point, reflective medium 55
ICC : Dumb profiles, smart CMM Profiles Measurements Viewing conditions Parameters (preferences,...) CMM Modeling devices Gamut mapping Appearance matching User preferences 56
Reference medium gamut RMG Union gamut several output processes Perceptual intent Primaries to be defined Defined in maximum chroma for L*-C* combinations RMG: Lightness 50 intersection 80 60 40 20 b* 0-20 -40-60 -80-100 -80-60 -40-20 0 20 40 60 80 100 a* 57
Reference medium gamut RMG: hue intersection 100 90 80 70 Lightness 60 0-180 degree 50 90-270 degree 40 30 20 10 0-150 -120-90 -60-30 0 30 60 90 120 150 Chroma 58
Reference medium gamut Hue Lightness 10 20 30 40 50 60 70 80 90 0 25 50 73 86 86 81 65 45 23 30 22 48 74 98 101 90 73 52 30 60 17 36 55 75 90 100 102 90 50 90 13 30 45 60 73 88 100 111 120 120 15 31 49 65 80 93 100 96 73 150 18 40 61 81 97 96 85 66 37 180 20 40 62 81 92 86 71 51 25 210 20 37 53 66 79 76 64 49 27 240 20 40 55 66 74 66 56 41 23 270 32 55 70 74 68 59 48 33 18 300 65 95 92 87 78 66 52 36 19 330 35 71 91 104 98 84 67 47 4 59
Without ICC 60
With ICC 61
Gamut Mapping and Digital Color Management Profile quality issues 62
Profile quality issues Yellow mapping often problematic Mapping has to be adapted Sampling points have to be well-chosen 63
Profile quality issues Lightness intersections offset- proofer Red Yellow Green Proofer Cyan Blue Magenta Offset 64
Profile quality issues Black dots are often too disturbing Extra gamut limitation required 65
Profile quality issues Additional black inks to reduce visibility of dots Min K Max K 66
Profile quality issues Results different ink usage significant ink reduction 7C 6C Cyan min K 6 inks Cyan Magenta Magenta Yellow Yellow Heavy Black Black Light Black Light Cyan Light Cyan Light Magenta Light Magenta max K 7 inks 67
References The Reproduction of Color, R. W. G. Hunt Color Science, Concepts and Methods, Quantitative Data and Formulae, Wyszecki and Stiles Color technology for electronic imaging devices, Henry Kang. Digital color halftoning, Henry Kang. Digital Color Management: Encoding Solutions, Giorgianni and Madden. Colour Engineering, edited by Phil Green and Lindsay MacDonald. Specification ICC.1:2004-10 (Profile version 4.2.0.0), Image technology colour management - Architecture, profile format, and data structure. 68