1. EE 638: Principles of Digital Color Imaging Systems Lecture 14: Monitor Characterization and Calibration – Basic Concepts

2. Color Imaging Systems CaptureProcessOutput Digital Camera Scanners Display: CRT LCD Projector Printers: Laser EP IJ Dye-sub Liquid EP offset RGB CMYK Device-dependent Goal: want colors to look same through out the system.

3. Which CMYK? – effect of rendering device Monitor HP DJ 970Cse Mac Driver HP DJ 970Cse IPP Driver

4. Which CMYK? – effect of capture device Olympus C3000 Digital Camera Heidelberg Scanner

5. Different color representations l Are they all equivalent? l How do we get from one to the other? l Can we get from one to the other? l Even if we can, what do all these numbers mean?

6. Example: capture to capture Given RGB values from one capture device, can we predict RGB values for a second capture device? Olympus C3000 Digital Camera RGB values (23, 136, 180) (203, 11, 52) (219, 186, 33) (7, 7, 7) Scanner RGB values (?, ?, ?)

7. Two Approaches to Color Management l 1. Closed pt-to-pt. solution l Separate mappings for each possible combination: C1  P1 C1  P2 C2  P1 C2  P2 Camera 1 Camera 2 Printer 1 Printer 2

8. Two Approaches (cont.) l 2. Standard Interchange Space Camera 1 Camera 2 Printer 1 Printer 2 Common Space CIE XYZ Device Dependent Space Device Dependent Space Device Independent Color Space

9. l Task: Find mapping for 1) Display (CRT & LCD) 2) Capture (cameras & scanners) 3) printers l Once we have pieces we can use a color management system (CMS) to implement everything. l Development of transforms for CRT displays. –Goal: given XYZ, find RGB that produces that XYZ Difficulty Increases CRT RGB CIE XYZ

10. l Two steps: –1) characterize device –2) invert mapping (calibration) l To do characterization need a device model DAC E-gun Digital Value Shadow Mask CRT

11. Magnified view of a shadow mask color CRT Magnified view of an aperture grille color CRT

12. 3 Phosphor Types P B  P G  P R  Primary Amounts

13. Overall (Forward) System Model l If primaries are visually independent, can find a 3x3 matrix, such that Desired color Necessary amount of primary NL1 NL2 NL3 Displayed CIE XYZ Input Digital Value Linear space of CRT monitor or LCD display

14. Single-Channel Excitation to Determine Nonlinearity l To get NLi, excite one channel at a time l Response for Y l Looking for (assume ) i.e. Digital Values are CRT RGB XYZ Color Measurement Device e.g. PR 705

15. Relation between Measured Y and Primary Amount + Multiplicative Scaling Assumption (constant) Note that this only works if changes to red channel model input multiplicatively scale the spectral power distribution

16. Offset-Gamma-Offset Model l Assumption is: –As I change Ri in monitor input –Output spectral distribution only changes by multiplicative constant l Typical model:

17. Monitor Characterization Process l To determine NL R, apply inputs for NL R NL G NL B Displayed CIE XYZ Input Digital Value Linear space of CRT monitor or LCD display CIE XYZ Color Measurement Device e.g. PR 705

18. Fitting Model to Data l Measure corresponding Y i values model for nonlinearity: l “off”  “offset” l Once we know NL R, NL G, NL B can determine matrix T l Let l Repeat for G, B to entire matrix NL R

19. Transformation between Linear RGB and CIE XYZ: Overdetermined Solution and Inclusion of Nonlinear Terms l To have more robust results, typically use a larger set input-output l Solve for T using least-squares for over-determined systems. l Generalization of model : –See Osman Arslan paper for example Measured Known Question: Why do we need these nonlinear terms?

20. Evaluating Accuracy of the Model l How do we evaluate accuracy of calibration? –Have a box (monitor) –Completed characterization: NL R, NL G, NL B, T Adjust Physical Device Effective Device Display

21. Evaluating Accuracy of the Model: Method 1 l Examine how well model fits data based on existing data or a subset thereof that was used to determine parameters NL R NL G NL B NL R NL G NL B Inverse Model Forward Model Proportional to Photon Count Linear Space Gamma Correction Gamma Corrected Space Gamma Uncorrection Linear Space

22. Evaluating Accuracy of the Model: Method 2 l Apply R, G, B (monitor space, device-dependent) inputs to physical device and measure actual output using colorimeter to get CIE l Compare with model predictions  test of forward model for device (characterization), but not calibration process (inverse model)

23. Evaluating Accuracy of the Model: Method 3 Calibration Process Physical Device PR705 Method 3a: Compare for some set of colors of interest, and compute Method 3b:Use human viewer to do qualitative assessment Pros: Accounts for entire system quantization (noise, instability …) or bottom line Cons: Requires measurements in lab, i.e. time and effort

24. Some local color history l 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 l Chuck Johnson –Left Textronix to join start-up Mead Imaging, Dayton, OH –Contacted me to do research on color in 1985 l Ron Gentile –Interned at Mead Imaging –Ph.D. Purdue, 1989 –Early employee at Adobe –Co-founded Bellamax

26. l text

27. Experimental results for gray balancing (NL i ) (Gentile et al, 1990)

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

29. 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