Color Management Using Device Models And Look-Up Tables Edward J. Delp School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana

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

Typical Delivery Of Visual Data Refinement (êr) W is set of visual stimuli, Z is set of digital images/video in common 24bit RGB. A digital image is only a collection of numbers. “Refinement” aims to improve the “look”. Analogous operation for motion pictures is color grading during post production. e є W Λ(.) i є Z Ψ(.) ê є W (visual data) (capture) (digital image) (display) (delivered data)

Need For Color Management ê1 ê2 ê3 êr 1. Color boundaries are only sumbolic. The artist would want: ê1 = ê2 = ê3 = êr

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 Custom displays are tech solutions (unlike engineering solutions) – expensive and not flexible. CMM is part of OS. Applications which support profiles request service from CMM.

Color Management Using Profiles Device 1 profile Device 2 profile Gamut mapping RGB1 image PCS PCS RGB2 image CMM Profile connection space (PCS) is typically CIE XYZ or CIE LAB CMM is part of the operating system

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 Profile format for WCS is a superset of ICC profile format, mostly backward compatible but all features work only on Vista. Traditionally, CMMs have been simple and rely on several LUTs to perform different tasks. But with greater computing power, more complex CMMs now frequently perform real time math – unsuitable for low-end devices. Color profile support in Firefox 3.0 (Courtesy: Deb Richardson)

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 Using EUR is like using profiles, we can obtain any other currency but sacrifice the advantage of a direct conversion. (Conversions courtesy: www.forex-rates.biz, as on June 6, 2009)

Motivation For The Proposed Method RGB Device 1 PCS PCS RGB Device 2 Gamut mapping Achieving visual similarity using profiles RGB Device 1 RGB Device 2 1. One step vs three step solution. Overall transformation f(.) Achieving visual similarity using the proposed model-based method

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 Chromatic adaptation is performed using Von Kries model.

Model-Based Color Transformation i є Z1 j є Z2 During white balancing, we ensure both devices have common white point D65. Monitor models consist of gray balancing and linear RGB-XYZ transform. Gray balancing by 1D LUTs, linear transform by an optimized 3x3 matrix. Gamut mapping uses Straight Chroma Clipping technique – retains lightness and hue. Non-Linear Transformation with White Point Correction (NLXWP) Monitor data by spectro-radiometric measurements

Model-Based Color Transformation The model NLXWP is represented by f (.) We use 3D look-up tables (LUT) to make it computationally more viable Let table look-up be represented by Φ (.) Input space is 256 × 256 × 256 We construct LUT by evaluating f at: Then, where £ represents 3D interpolation

Optimal 3D 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 1. Training set contained 1000 random (R,G,B) values. 2. Eight-point interpolation with weights proportional to square of inverse Euclidean distance in 3D space.

Color Management Using 3D 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 Reference device can be specified as metadata in the content. An artist can decide on a reference when editing the content. His/her display device will then work using a 3D LUT corresponding to the reference. Later, any user viewing the content will also using a LUT for the same reference.

Color Management Using 3D 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 × 21 Evaluate accuracy of much smaller LUTs In terms of over a testing set Assuming NLXWP is accurate 1. 21x21x21 = 9,261 < 10,000 < (2*15KB)/3: 2 profiles of 15KB, memory divided equally between RGB channels.

Color Management Using 3D LUT Testing error statistics in ΔE units An optimal 12 × 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

Future Work Have shown that device models and 3D 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 Multiple (likely two) LUTs may be used for each reference corresponding to user device being in illuminated or dark rooms. We may mention that LUTs should be able to handle small changes in display settings (brightness, etc) by the user. One solution is to make LUTs that work over a range of display settings. Alternatively, give the CMM ability to adjust LUT entries due to these changes.