EE 638: Principles ofDigital Color Imaging Systems

Lecture 14: Monitor Characterization and Calibration – Basic Concepts

Color Imaging Systems

Capture Process Output

Digital CameraScanners

Display: CRT LCD Projector

Printers: Laser EP IJ Dye-sub Liquid EP offset

RGBRGB

CMYK

Device-dependent

Goal: want colors to look same through out the system.

Which CMYK? – effect of rendering device

MonitorHP DJ 970Cse

Mac DriverHP DJ 970Cse

IPP Driver

Which CMYK? – effect of capture device

Olympus C3000Digital Camera Heidelberg Scanner

Different color representations

Are they all equivalent? How do we get from one to the other? Can we get from one to the other? Even if we can, what do all these numbers mean?

Example: capture to capture

Given RGB values from one capture device, can we predict RGB values for a second capture device?

Olympus C3000Digital Camera

RGB values

(23, 136, 180)

(203, 11, 52)

(219, 186, 33)

(7, 7, 7)

Scanner

RGB values

(?, ?, ?)

(?, ?, ?)

(?, ?, ?)

(?, ?,?)

Two Approaches to Color Management

1. Closed pt-to-pt. solution

Separate mappings for each possible combination:

C1 P1

C1 P2

C2 P1

C2 P2

Camera 1

Camera 2

Printer 1

Printer 2

Two Approaches (cont.)

2. Standard Interchange Space

Camera 1

Camera 2

Printer 1

Printer 2

Common Space

CIE XYZ

1CT

2CT

1PT

2PT

Device Dependent Space

Device Dependent Space

Device Independent Color Space

Task: Find mapping for1) Display (CRT & LCD)

2) Capture (cameras & scanners)

3) printers

Once we have pieces we can use a color management system (CMS) to implement everything.

Development of transforms for CRT displays.

– Goal: given XYZ, find RGB that produces that XYZ

Difficulty Increases

CRTRGBCIEXYZ

Two steps:– 1) characterize device– 2) invert mapping (calibration)

To do characterization need a device model

DAC

DAC

DAC

E-gun

E-gun

E-gun

RV

GV

BV

R

B

R

0 255Digital Value

Shadow Mask

CRT

CRT

Magnified view of a shadow mask color CRT

Magnified view of an aperture grille color CRT

3 Phosphor Types

P B

P G

P R

( ) ( ) ( ) ( )R R G G B BD a P a P a P

( )R G Ba a a Primary Amounts

Overall (Forward) System Model

If primaries are visually independent, can find a 3x3 matrix , such that

3 3T

1T

1

R

G

B

a X

a T Y

a Z

Desired colorNecessary amount of primary

NL1

NL2

NL3

R

G

B

lR

lG

lB

X

Y

Z

DisplayedCIE XYZ

InputDigital Value

Linear space of CRT monitor or LCD display

Single-Channel Excitation to Determine Nonlinearity

To get NLi , excite one channel at a time

Response for Y

Looking for (assume )

( ) ( )R RD a P

( ) ( )Y y D d

i.e. Digital Values are 0 0iR

CRTRGB XYZ

Color Measurement Device e.g. PR 705

( ), 1...li R iR NL R i N

lRR a

Relation between Measured Y and Primary Amount + Multiplicative Scaling Assumption

( ) ( )

( ) ( ) ( )

( ) ( ) ( )

i

R i R

R i R

Y y D d

y NL R P d

NL R y P d

( )

( ) ( )R R

R i R

a P

NL R P

RY (constant)iY

iR

Note that this only works if changes to red channel model input multiplicatively scale the spectral power distribution

Offset-Gamma-Offset Model

Assumption is:– As I change Ri in monitor input– Output spectral distribution only changes by multiplicative

constant

Typical model:

( 0 0)iR( )D

Ra

( )255

offinR offout offin

R

offout offin

R Rc R R R

aR R R

(0,0,0)

(1,0,0)

(2,0,0)

0

1

2

y

y

y

Monitor Characterization Process

To determine NLR , apply inputs for

3 3T

NLR

NLG

NLB

R

G

B

lR

lG

lB

X

Y

Z

DisplayedCIE XYZ

InputDigital Value

Linear space of CRT monitor or LCD display

,0,0iR 0,16,32,..., 255iR

CIE XYZ

Color Measurement Device e.g. PR 705

RGB

Fitting Model to Data

Measure corresponding Yi values model for nonlinearity:

“off” “offset” Once we know NLR, NLG, NLB can determine matrix T

Let

Repeat for G, B to entire matrix

255offin

R offout offinR

offout offin

R RC R R R

a

R R R

0 11

12

13

255

0 0

0 0

lR T

R T

T

NL R

Transformation between Linear RGB and CIE XYZ: Overdetermined Solution and Inclusion of Nonlinear Terms To have more robust results, typically use a larger set

input-output

Solve for T using least-squares

for over-determined systems.

Generalization of model :– See Osman Arslan paper

for example

Measured Known

2

2

3 10 2

( )

( )

( )

( )

( )

( )

( )

l

l

l

l

l

l

l l

l l

l l

l l l

R

G

B

RX

GY T

BZ

R G

G B

R B

R G B

Question: Why do we need these nonlinear terms?

Evaluating Accuracy of the Model

How do we evaluate accuracy of calibration?– Have a box (monitor)

– Completed characterization: NLR, NLG, NLB, T

orig

orig

orig

R

G

B

adj

adj

adj

R

G

B

AdjustPhysical Device

X

Y

Z

Effective Device Display

Evaluating Accuracy of the Model: Method 1

Examine how well model fits data based on existing data or a subset thereof that was used to determine parameters

arg

arg

arg

t et

t et

t et

X

Y

Z

l

l

l

R

G

B

1T 1T

NLR-1

NLG-1

NLB-1

R

G

B

NLR

NLG

NLB

actual

actual

actual

X

Y

Z

Inverse Model Forward Model

Proportional to Photon

Count

LinearSpace

GammaCorrection

GammaCorrectedSpace

GammaUncorrection Linear

Space

Evaluating Accuracy of the Model: Method 2

Apply R, G, B (monitor space, device-dependent) inputs to physical device and measure actual output using colorimeter to get CIE

Compare with model predictions

test of forward model for device (characterization),

but not calibration process (inverse model)

actual actual actualX Y Z

Evaluating Accuracy of the Model: Method 3

arg

arg

arg

t et

t et

t et

X

Y

Z

'

'

'

R

G

B

Calibration Process

Physical Device

PR705 actual

actual

actual

X

Y

Z

Method 3a: Comparefor some set of colors of interest, and compute

Method 3b:Use human viewer to do qualitative assessment

E

Pros: Accounts for entire system quantization (noise, instability …) or bottom line

Cons: Requires measurements in lab, i.e. time and effort

Some local color history John Dalton

– MSEE University of Delaware, circa 1983

– Worked for Textronix, Wilsonville, OR on inkjet printers with Chuck Johnson (Zhen He worked there, now at Intel)

– Worked for Apple with Gary Starkweather (inventor of laser printer, now at Microsoft)

– Founded Synthetik and moved to Hawaii

Chuck Johnson– Left Textronix to join start-up Mead Imaging, Dayton, OH

– Contacted me to do research on color in 1985

Ron Gentile– Interned at Mead Imaging

– Ph.D. Purdue, 1989

– Early employee at Adobe

– Co-founded Bellamax

text

Experimental results for gray balancing (NLi) (Gentile et al, 1990)

Experimental results for forward model (Gentile et al, 1990)

Additional resource for display device characterization and calibration

130904 Minh_Nguyen_Monitor_Calibration.pptx (can be found in Reference section of course website)

Features– Summary and review of work by Arslan, Thanh, and Min– Detailed discussion of how to set white point– Description of three different models for gray balance curve

• Gamma-based• Two part gamma-based• Spline curve

– Recent experimental results• Achieves 4 Delta E average error with gamma-based• Less than 2 Delta E average error with other two methods

listed above• Documents day-to-day variability