Color Management Using Device Models And Look-Up Tables Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana June 19, 2009 Slide 1
Outline • Introduction – Color Management – Current Practices • Motivation For The Proposed Method • Constructing A Transformation Based on Device Models • Color Management Using Look-Up Tables • Conclusions And Future Work June 19, 2009 Slide 2
Typical Delivery Of Visual Data Λ(. ) eєW (visual data) Refinement (êr) iєZ Ψ(. ) êєW (capture) (digital image) (display) (delivered data) June 19, 2009 Slide 3
Need For Color Management ê 1 ê 2 ê 3 êr The artist would want: ê 1 = ê 2 = ê 3 = êr June 19, 2009 Slide 4
Current Approaches To Color Management • Solutions for still digital images – Capture and display device profiles – Color management module (CMM) • Solutions for professionally screened digital video – Target display environment (cinema theatre projection) is known – Artists need to view content in similar environment during post-production – Custom-built display devices • We focus on profile based solutions June 19, 2009 Slide 5
Color Management Using Profiles Device 2 profile Device 1 profile RGB 1 image Gamut mapping PCS RGB 2 image CMM • Profile connection space (PCS) is typically CIE XYZ or CIE LAB • CMM is part of the operating system June 19, 2009 Slide 6
Motivation For An Alternative • All applications do not support profiles • Vendors are going beyond ICC profiles – Vista’ WCS • Complex CMM not suitable for portable devices Color profile support in Firefox 3. 0 June 19, 2009 (Courtesy: Deb Richardson) Slide 7
Motivation For An Alternative • Consider an analogy with currency exchange • GCIS participant from China – 100 CNY 10. 2736 EUR – 10. 2736 EUR 91. 4064 NOK – 100 CNY 91. 6179 NOK • Multi-step conversions – Lower accuracy, slower – Not suitable for multiple/continuous transactions (Conversions courtesy: www. forex-rates. biz, as on June 6, 2009) June 19, 2009 Slide 8
Motivation For The Proposed Method RGB Device 1 PCS RGB Device 2 Gamut mapping Achieving visual similarity using profiles RGB Device 1 RGB Device 2 Overall transformation f(. ) Achieving visual similarity using the proposed model-based method June 19, 2009 Slide 9
Model-Based Color Transformation • Consider two display devices Ψref and Ψuser and a digital visual data i є Z • Construct a function f: Z Z such that Ψref( i) ≈ Ψuser( f (i)) where ≈ represents visual similarity • We consider similarity in a perceptual sense – Account for different viewing conditions and chromatic adaptation – Match the color outputs in CIE LAB space June 19, 2009 Slide 10
Model-Based Color Transformation i є Z 1 j є Z 2 • Non-Linear Transformation with White Point Correction (NLXWP) • Monitor data by spectro-radiometric measurements June 19, 2009 Slide 11
Model-Based Color Transformation • The model NLXWP is represented by f (. ) • We use 3 D look-up tables (LUT) to make it computationally more viable • Let table look-up be represented by Φ (. ) – Input space is 256 × 256 • We construct LUT by evaluating f at: • Then, where £ represents 3 D interpolation June 19, 2009 Slide 12
Optimal 3 D LUT • Given a function f and a size specification G (number of entries) for the LUT • Accuracy of Φ depends on choice of Ω and £ – Measure accuracy with respect to f – Formulate it as an optimization problem • Let, Then over a training set, • Obtain parameters so as to minimize June 19, 2009 Slide 13
Color Management Using 3 D LUT • Assumption: There is always an intended reference device for visual content – Digital Cinema (DCI) Reference Projector for motion pictures • With the knowledge of Ψref construct a function f (NLXWP) for any given display Ψuser • Use f to obtain optimal LUT Φ for this device pair • Color correct content using Φ whenever this reference device is specified June 19, 2009 Slide 14
Color Management Using 3 D LUT • Compare the performance of LUT based color management with ICC profiles – Profiles vary from 0. 5 KB to over 0. 5 MB in size – Consider average size profiles ~15 KB each for the reference and user device – Comparable largest LUT will be 21 × 21 • Evaluate accuracy of much smaller LUTs – In terms of over a testing set – Assuming NLXWP is accurate June 19, 2009 Slide 15
Color Management Using 3 D LUT Testing error statistics in ΔE units • An optimal 12 × 12 LUT keeps mean error below 2 ΔE using less than 20% of the designated memory • Non-optimal LUT uses tri-linear interpolation and uniform sampling of RGB space June 19, 2009 Slide 16
Future Work • Have shown that device models and 3 D LUT can be used for color management • Compare with ICC profile-based color management system – Accuracy and speed – Expect improvement in both: specific solution is accurate, LUT is faster • Design a collection of LUTs corresponding to different reference devices – User device can select the appropriate LUT June 19, 2009 Slide 17
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