1 A novel scheme for color-correction using 2-D Tone Response Curves (TRCs) Vishal Monga ESPL Group Meeting, Nov. 14, 2003.

Slides:



Advertisements
Similar presentations
Normal Map Compression with ATI 3Dc™ Jonathan Zarge ATI Research Inc.
Advertisements

Slide 1CPU Emulator Tutorial This program is part of the software suite that accompanies the book The Digital Core, by Noam Nisan and Shimon Schocken 2003,
Inpainting Assigment – Tips and Hints Outline how to design a good test plan selection of dimensions to test along selection of values for each dimension.
Digital Image Processing
Grey Level Enhancement Contrast stretching Linear mapping Non-linear mapping Efficient implementation of mapping algorithms Design of classes to support.
ECE 472/572 - Digital Image Processing Lecture 10 - Color Image Processing 10/25/11.
Full Gamut Color Matching for Tiled Display Walls Grant Wallace, Han Chen, Kai Li Princeton University.
CSCI 347 / CS 4206: Data Mining Module 07: Implementations Topic 03: Linear Models.
FTP Biostatistics II Model parameter estimations: Confronting models with measurements.
Image Enhancement To process an image so that the result is more suitable than the original image for a specific application. Spatial domain methods and.
Multi-media Graphics JOUR 205 Color Models & Color Space 5 ways of specifying colors.
1 Digital Logic
Camerabased projector calibration, investigation of the Bala method
Graphics File Formats. 2 Graphics Data n Vector data –Lines –Polygons –Curves n Bitmap data –Array of pixels –Numerical values corresponding to gray-
Color Management Systems Problems –Solve gamut matching issues –Attempt uniform appearance Solutions –Image dependent manipulations (e.g. Stone) –Device.
Quantization If too few levels of gray, (e.g. decrease halftone spot size to increase spatial resolution), then boundaries between adjacent gray levels.
Fundamentals of Multimedia Chapter 4 Color in Image and Video Ze-Nian Li and Mark S. Drew 건국대학교 인터넷미디어공학부 임 창 훈.
Color Fidelity in Multimedia H. J. Trussell Dept. of Electrical and Computer Engineering North Carolina State University Raleigh, NC
Data Storage Technology
Vector vs. Bitmap SciVis V
Dye Sublimation Color Management
Selecting the Right Color Palette: Understanding RGB and CMYK Color Presented by Pat McClure and Tony Kugler.
V Obtained from a summer workshop in Guildford County July, 2014
Color Systems. Subtractive Color The removal of light waves to perceive color: –Local or physical attributes of pigments, dyes, or inks reflect certain.
Colour Digital Multimedia, 2nd edition Nigel Chapman & Jenny Chapman
Digital Multimedia, 2nd edition Nigel Chapman & Jenny Chapman Chapter 6 This presentation © 2004, MacAvon Media Productions Colour.
Understanding Colour Colour Models Dr Jimmy Lam Tutorial from Adobe Photoshop CS.
Navigating and Browsing 3D Models in 3DLIB Hesham Anan, Kurt Maly, Mohammad Zubair Computer Science Dept. Old Dominion University, Norfolk, VA, (anan,
2001 by Jim X. Chen: 1 The purpose of a color model is to allow convenient specification of colors within some color gamut.
File Management Chapter 12. File Management File management system is considered part of the operating system Input to applications is by means of a file.
Doc.: IEEE /0330r2 SubmissionSameer Vermani, QualcommSlide 1 PHY Abstraction Date: Authors: March 2014.
Colours and Computer Jimmy Lam The Hong Kong Polytechnic University.
Vector vs. Bitmap
Validation of Color Managed 3D Appearance Acquisition Michael Goesele Max-Planck-Institut für Informatik (MPI Informatik) Vortrag im Rahmen des V 3 D 2.
Computer Science Term 1, 2006 Tutorial 5 The Final Exam.
Consistent Presentation of Images
Optimal XOR Hashing for a Linearly Distributed Address Lookup in Computer Networks Christopher Martinez, Wei-Ming Lin, Parimal Patel The University of.
1. Outline Introduction Related work on packet classification Grouper Performance Analysis Empirical Evaluation Conclusions 2/42.
© Janice Regan, CMPT 300, May CMPT 300 Introduction to Operating Systems Memory: Relocation.
CHAPTER 8 Color and Texture Mapping © 2008 Cengage Learning EMEA.
Precise and Approximate Representation of Numbers 1.The Cartesian-Lagrangian representation of numbers. 2.The homotopic representation of numbers 3.Loops.
Computer Graphics An Introduction Jimmy Lam The Hong Kong Polytechnic University.
Ch 6 Color Image processing CS446 Instructor: Nada ALZaben.
EE 638: Principles of Digital Color Imaging Systems Lecture 14: Monitor Characterization and Calibration – Basic Concepts.
 By Bob “The Bird” Fiske & Anita “The Snail” Cost.
Domain decomposition in parallel computing Ashok Srinivasan Florida State University.
Basic Computer Organization Rashedul Hasan.. Five basic operation No matter what shape, size, cost and speed of computer we are talking about, all computer.
Presented By : Dr. J. Shanbezadeh
BASIC COLOUR COURSE Algemeen
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
MultiModality Registration Using Hilbert-Schmidt Estimators By: Srinivas Peddi Computer Integrated Surgery II April 6 th, 2001.
Simulation and Experimental Verification of Model Based Opto-Electronic Automation Drexel University Department of Electrical and Computer Engineering.
Data Visualization Fall The Data as a Quantity Quantities can be classified in two categories: Intrinsically continuous (scientific visualization,
ECE 638: Principles of Digital Color Imaging Systems Lecture 5: Primaries.
1 Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX USA 1 Mr. Vishal Monga,
Guilford County SciVis V104.03
Color Measurement and Reproduction Eric Dubois. How Can We Specify a Color Numerically? What measurements do we need to take of a colored light to uniquely.
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Integrated Color Solutions A presentation.
Mark Dorman – UCL/RAL – Calibration Workshop Talk Update on ND Strip-to-Strip Calibration Work Mark Dorman Calibration Workshop Fermilab, September 7-9.
CS522 Advanced database Systems
Vector vs. Bitmap.
EE 638: Principles of Digital Color Imaging Systems
School of Electrical and
4. All About Profiles 5. Measurement, Calibration, and Process Control 6. Building Display Profiles GED119 Colour Science and Digital Applications.
mEEC: A Novel Error Estimation Code with Multi-Dimensional Feature
Classification Slides by Greg Grudic, CSCI 3202 Fall 2007
Basic Concepts of Digital Imaging
Gradient Domain Salience-preserving Color-to-gray Conversion
Presentation transcript:

1 A novel scheme for color-correction using 2-D Tone Response Curves (TRCs) Vishal Monga ESPL Group Meeting, Nov. 14, 2003

2 Outline Device Calibration & Characterization One-dimensional Calibration – Typical Approaches – Merits and Limitations Two-dimensional Color-Correction – Basic Concept – Applications – calibration – stability control – device emulation

3 Why characterization & calibration? Different devices capture and produce color differently

4 Why characterization & calibration? Produce consistent color on different devices

5 Device Independent Paradigm

6 Printer Calibration and Characterization Calibration – Tune device to a desired color characteristic – Typically done with 1-D TRCs Characterization – Derive relationship between device dependent and device independent color – Forward characterization – given CMYK, predict CIELAB response (based on a printer model) – Inverse characterization – given an input CIELAB response, determine CMYK required to produce it

7 Partitioning the device-correction Characterization Calibration Output Device Calib.CMYK Archival/ Fast Re-print Path Device Independent Color “Calibrated” CMYK Device CMYK Device-correction-function “Calibrated Device” Alternate CMYK (fast emulation) Motivation – Some effects e.g. device drift may be addressed (almost) completely via calibration – Calibration requires significantly lower measurement and computational effort

8 One-Dimensional Calibration Two major approaches – Channel Independent – Gray-Balanced Calibration Channel Independent – Each of C, M, Y and K separately linearized to a metric e.g. Optical density or  E from paper – Ensures a visually linear response along the individual channels

9 Channel wise linearization ………. Device Raw Response One-dimensional TRCs

10 Channel wise Linearization …. Testing CMYK sweeps Calibrated Printer response

11 Gray-balance Calibration Goal: C=M=Y must produce gray/neutral – search for CMY combinations producing a*= b*=0 – Also capable of handling user-specified aim curves

12 One-Dimensional Calibration : Analysis Very efficient for real-time color processing – For 8 bit processing just 256 bytes/channel – Very fast 1-D lookup So what’s the problem? – Device gamut is 3-dimensional (excluding K) – We only shape the response along a one- dimensional locus i.e. very limited control

13 1-D Calibration : Analysis …….. Example: 1-D TRCs can achieve gray-balance or channel-wise linearity but not both

14 1-D Calibration : Analysis …….. Gray-balance lost with channelwise linearization a* vs C=M=Y=db* vs C=M=Y=d

15 Alternatives Use a complete characterization – 3-D (or 4-D) look-up tables (LUTs) involve no compromises – Expensive w.r.t storage and/or computation – Require more measurement effort Explore an intermediate dimensionality – 2-D color correction – Requirements: Must be relatively inexpensive w.r.t computation, storage & measurement effort

16 Two-Dimensional Color Correction 2-D TRCs instead of 1-D TRCs 2D TRC Calibration determined 2D TRCs C M Y Calibration Transform C’ 2D TRC v i1 (C,M,Y) M’ v i2 (C,M,Y) Y’ v i3 (C,M,Y) Fixed Transforms 2D TRC

17 Example of 2-D Color Correction Cyan 2-D LUT: – Specify desired response along certain 1-D loci – Interpolate to fill in the rest of the table – LUT size = 256 x 511 = 128 kB/channel 0 x Control along device Gray (C = M = Y) Control along device secondary axis (e.g. C = M, Y = 0) Control along primary Control along device secondary to black C M + Y Control along primary to black

18 Example of 2-D Color Correction C M Y Calibration Transform C’ M + Y v i1 (C,M,Y) M’ v i2 (C,M,Y) Y’ v i3 (C,M,Y) Fixed Transforms Calibration determined 2D TRCs C C + Y M C + M Y Linearization 1-D TRC K’K

19 Application to Device Calibration

20 Application to Device Calibration Enables greater control in calibration – e.g. linearization and gray-balance simultaneously – More generally, arbitrary loci in 2-D space can be controlled to arbitrary aims A geometric comparison with 1-D – 1-D: An entire plane C=C 0 maps to same output C’ – 2-D: A line in 3-D space (intersection of planes C=C 0, M+Y = S 0 ) maps to same output C’

21 Visualization of 1-D Vs 2-D calibration

22 Results Hardcopy Prints – Fig. 1, 1D linearization TRC (deltaE from paper) – Fig. 2, 1D gray-balance TRC – Fig. 3, 2-D TRCs

23 Application to Stability Control

24 Experiment Build calibration & characterization at time T 0 – Print & measure a CIELAB target, compute  E between input and measured CIELAB values – Repeat at time T 1 (>> T 0 ) for different calibrations (e.g. 1-D deltaE, gray-balance, 2-D) Calibration (updated) Characterization (static) Print & measure LAB target within device gamut Error metric calculation  E LAB Values CMYK

25 Results Printer : Phaser 7700 Times: T 0 = Aug 1 st T 1 = Aug 20 th Correction Derived at Measured at Average  E 94 error 95%  E 94 error 1-D gray-balance + characterization T0 T0 T0 T D channel independent T1 T1 T1 T D gray-balance T1 T1 T1 T D T1 T1 T1 T No recalibration T0 T0 T1 T

26 Application to Device Emulation

27 Device Emulation Make a target device ``emulate” a reference – Reference could be another device – printer/display – Or a mathematical idealization (SWOP)

28 SWOP emulation on Xerox CMYK Problem: – SWOP rich black requires high C,M,Y – Xerox CMYK rich black requires low C,M,Y 1-D TRCs for emulation – Monotonic  cannot preserve rich black 4-D SWOP CMYK  Xerox CMYK – Accurate, but costly for high speed printing 2-D emulation – A good tradeoff?

29 Partial 2-D Emulation Use 4-D emulation as “ground truth” to derive 2-D TRCs 2-D Emulation LUTs are: C vs. M+Y M vs. C+Y Y vs. C+M K vs. min(C,M,Y) K addition 4  4 emulation LUT CMY control point C M + Y SWOP CMYK 2D TRC for Cyan Xerox CMYK Fill in C value SWOP GCR

30 Visualization of emulation transform

31 Emulation : Results 1D2D 4D

32 Conclusions 2-D color correction – Enables significantly greater control than 1-D – Implementation cost > 1-D but << 3/4-D – Addresses a variety of problems – Calibration – Stability Control – Device Emulation References – V. Monga, R. Bala and G. Sharma, ``Two-dimensional transforms for device color calibration'', Proc. SPIE/IS&T Conf. On Color Imaging, Jan , 2004

33 Back Up Slides

34 2-D Calibration : Response Shaping

35 SWOP Emulation on iGen How to populate the 2-D table(s) ? – Specify 1-D swop2igen type corrections along various axis (wherever possible) and interpolate? – Experiments show interpolating gives a poor approximation to the response K min(C,M,Y) Example K’ is substantial Almost no K’ Interpolating between 1-D loci does not capture this behavior

36 SWOP Emulation on iGen Instead populate by “brute force” mimicking of the 4-dimensional response – For the K table, treat min(C,M,Y) axis as C=M=Y (approximately a measure of input black) – Run equal CMY sweeps for each K through 4-D corrections & fill the K table with the results C, M, Y tables are trickier – Need to fold GCR into the table as well – C’ (corrected Cyan) must be a function of (C, M+Y) as well as K

37 SWOP Emulation on iGen 510 G,B M + Y C M,Y For each C = i, i = 0, 1, … 255 (1) increase M up to i, Y = 0 (2) increase Y up to C=M=Y=i (3) increase M from i … 255 & (4) increase Y from i … 255, add K in sweeps according to a SWOP like GCR Red black white

38 K min(C,M,Y) K’ = f (K, min(C,M,Y) ) SWOP Emulation on iGen - the K channel

39 Implementation ALI scripts to derive 2-D TRCs Calibration: – Core routine: get2DTRCs.ali – Support routines: stretchTRCs.ali, tuneGrayTRCs.ali, fittrc2maxgray.ali – 2-D TRCs written as an ELFLIST of ELFOBJECTS (in this case CTK LUT objects) Emulation: – 2Demuln.ali, make2DTRCK.ali