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ECE 638  Principles Of Digital Color Imaging Systems  Purdue Study Resources

10. Discrete Wavelength Models Characterization Of Human Subspace  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 10: Discrete Wavelength Models Characterization of Human Visual Subspace Synopsis Review of discrete wavelength model Discretewavelength trichomatic model Stimulus n 31 1 Sensor response si 31

09. Discrete Wavelength Models Projection Operator  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 9: Discrete Wavelength Models Projection Operator Synopsis Review of discrete wavelength model Discretewavelength trichomatic model Stimulus n 31 1 si 31 1, i=1,2,3 Sensor response T Response

08. Discrete Wavelength Models Primaries  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 8: Discrete Wavelength Models Primaries Synopsis Review of discrete wavelength model Definition of primary set for discrete wavelength model Color matching condition and graphical interpretation

07. Discrete Wavelength Models Sensors  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 7: Discrete Wavelength Models Sensors Synopsis Discretization of model Sensor subspace Decomposition of stimulus into fundamental and nullspace components Graphical interpretation Why discretize

06. CIE Standards  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 6: CIE Standards Synopsis Brief review of primaries Chromaticity diagram for primaries Overview of CIE Photometry and relative luminous efficiency function CIE 1931 standard observer RGB form

05. Primaries  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 5: Primaries Synopsis Brief review of sensor concepts Spectral model for additive primary mixtures Computation of match amounts Transformation between primaries Color matching functions Review:

04. Chromaticity Diagram  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 4: Chromaticity Diagram Goal is to understand the origins and meaning of everyday chromaticity diagrams, such as the CIE xy diagram Synopsis Brief review of trichromatic theory Develop chromatic

03. Trichromatic Theory  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 3: Trichromatic theory of color Synopsis Continue development of mathematical framework for trichromatic theory Review: foundations for trichromatic theory Color matching experiment Grassmans la

02. Foundations  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 2: Foundations of Color Science Synopsis Briefly trace development of our knowledge of color and vision; so that we can discover it in a logical manner. Explore concepts of color matching that f

01. Introduction  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Jan P. Allebach School of Electrical and Computer Engineering allebach@purdue.edu What is a digital color imaging system? What else besides printers? Digital cameras Flatbed scanners Mobile phones Perso

Hw6_sol
School: Purdue

Hw6
School: Purdue
Due 5:00 PM Wednesday, 4/28/99 EE 695A 1. Homework No. 6 Spring 1999 Consider the periodic halftone dot array shown below. Suppose that each dot occupies a fraction a of the area within its square cell. Assume that the dots have no surface reflectance, an

Hw5_sol
School: Purdue

Hw5
School: Purdue
Due 5:00 PM Monday, 4/12/99 EE 695A 1. Homework No. 5 Spring 1999 Shown below is the index matrix for a 3x3 halftone screen 4 1 5 3 7 2 0 8 6 a. b. 2. Assuming the continuoustone image is expressed in units of absorptance, find the corresponding threshol

Hw4_sol
School: Purdue

Hw4
School: Purdue
Due 5:00 PMMonday, 4/5/99 EE 695A Homework No. 4 Spring 1999 Colors along the achromatic axis are of special interest in design of systems for good color reproduction. The next two problems deal with two different aspects of the achromatic axis. 1. Good c

Hw3_sol
School: Purdue

11. Color Opponency  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 11: Color Opponency Basic spatiochromatic model structure L Stimulus Trichromatic Stage O1 ~ O1 M O2 ~ O2 Opponent Stage S O3 Spatial Frequency Filtering Stage ~ O3 Opponent stage Trichromatic

12. Characterization Of Illuminants  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 12: Characterization of Illuminants Synopsis Review of HVS color model Wandells Model Trichromatic Stage Opponent Color Stage Equivalent Representation: CIE XYZ (standard space for Colorimetry)

28. Design_of_color_periodic_clustereddot _creen_sets
School: Purdue
3.3 Screening Part 3 Advanced Topics in Digital Halftoning 2830 October 2008 3.3.1 Synopsis Screening architecture Screen taxonomy Screen descriptors Analysis of tone and detail Stochastic screens Multilevel screens Periodic, clustereddot screens (hybri

27. Mediacolorant Interactions  Color
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 25 KruseGustavon model Microscopic model Linear system colorant (4) (1) (3) (2) Layer 1 substrate Model assumptions for Layer1 Within colorant, assume ideal pointtopoint interaction for sca

26. Mediacolorant Interactions  Monochrome
School: Purdue
Eq. (1) total reflectance From Eq. (1) on Slide 5 recall: a = 0, r = 1, D = 0 a = 0.1, r = 0.9, D = 0.046 a = 0.9, r = 0.1, D = 1 a = 0.99, r = 0.01. D = 2 1 log_10(x) 1 x Page 9a MurrayDaviesequa.onintermsof reectance r = 1 a ( ) = 1 ( A (1 r ) + A (1 r

25. Halftoning_screen  2011
School: Purdue
4. Screening Advanced Digital Halftoning 2 October 2011 4.1 Synopsis Screening architecture Screen taxonomy Screen descriptors Analysis of tone and detail Stochastic screens Periodic, clustereddot screens (hybrid screen) Moirefree conditions for screen

24 Halftoning_error_diffusion  2011
School: Purdue
3. Error Diffusion Advanced Digital Halftoning 2 October 2011 3.1 Synopsis Error diffusion architecture Error diffusion textures Edge enhancement effect of error diffusion Spectral analysis of error diffusion Variations on a theme Tonedependent error dif

23. Halfoning_DBS  2011
School: Purdue
2. Direct Binary Search (DBS) Advanced Digital Halftoning 2 October 2011 2.1 Outline Overview of searchbased halftoning methods DBS framework DBS behavior Efcient implementation for DBS Dual interpretation for DBS DBS with embedded printer models Electr

22. Halftoning_fundamentals  2011
School: Purdue
1. Halftoning Fundamentals Advanced Digital Halftoning 2 October 2011 1.1 Outline Goals of halftoning Taxonomy of halftoning textures Architectures for halftoning algorithms Trainingbased approaches to algorithm design Impact of printer and viewer Halfto

21. Halftoning_intro  2011
School: Purdue
Advanced Digital Halftoning Jan Allebach School of Electrical and Computer Engineering Purdue University West Lafayette, IN 479072035 allebach@purdue.edu Advanced Digital Halftoning 2 October 2011 0.1 Course Outline 1. 2. 3. 4. Halftoning Fundamentals Di

20. Display And Printing  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 22: Display and Printing Synopsis General Model for Display and Printing Processes LineContinuous Scanning Systems MatrixAddressable Systems General Model Ideal Reconstruction Zero Order Hold R

19. Scanning And Sampling  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 21: Sampling and Scanning Synopsis Scanning Technologies Development of a general model Analysis sampling Sampling on arbitrary lattices Analysis of scanning Terminology: Sampling  mapping from

18. 2D Lnear Systems And Spectral Analysis  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 20: 2D Linear Systems and Spectral Analysis Synopsis Special 2D Signals 2D Continuousspace Fourier Transform (CSFT) Linear, Shiftinvariant Imaging Systems Periodic Structures Special 2D Sig

17. Digital Camera Characterization And Calibration And Basic  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 15: Image Capture Image Capture Devices Image Capture Devices: Scanners Camera Camera: Sensor Array Sensor array technologies CCD from a HP CCD (Charge Coupled Device) digital camera CID (Cha

16. Scanner Characterization And Calibration  2011
School: Purdue
Lecture 16 Scanner Characterization and Calibration  Sanjyot Gindi M.S.E.C.E, Purdue University July 18th 2008 Objectives: Spectral model based characterization of Samsung SCX5530 scanner. Empirical or regression based characterization of Samsung, HP Pho

15. Monitor Characterization And Calibration Advanced  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 16: Monitor Characterization and Calibration Advanced Methods Monitor Characterization Method Using Multiple Nonsquare Matrices Thanh Ha and Satyam Srivastava (2009) Modelbased monitor model Fo

14. Monitor Characterization And Calibration Basic  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 14: Monitor Characterization and Calibration Basic Concepts Color Imaging Systems Capture Process Output Digital Camera Scanners RGB Display: CRT LCD Projector RGB Devicedependent Printers: Lase

13. Uniform Color Spaces  2011
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Lecture 13: Uniform Color Spaces Synopsis Overall HVS model thus far is linear Psychometric function Question: Does Bag in right hand weigh more than Bag in left hand? Yes or No? Probit Analysis Slope m

Hw3
School: Purdue
Due 5:00 PM Friday, 3/5/99 EE 695A 1. Homework No. 3 Spring 1998 Shown below is the 1931 CIE x y chromaticity diagram where C denotes the chromaticity of Illuminant C and A and B are points corresponding to two arbitrary stimuli. Also shown is the dayligh

Hw2_sol
School: Purdue

Hw2
School: Purdue
Due 5:00 PM Wednesday, 2/17/99 EE 695A 1. Homework No. 2 Spring 1998 Consider a finite dimensional model for a linear, bichromatic vision system. Assume that we sample at N = 3 wavelengths. Supppose that the sensor response matrix is given by 1 S = 0 0 a.

49. Measuring Image Artifacts  JPEG Ringing  2011
School: Purdue
Measurement of Ringing Artifacts in JPEG Images* Xiaojun Feng Jan P. Allebach Purdue University  West Lafayette, IN * Research supported by the HewlettPackard Company EI 2006  San Jose, CA Slide No. 1 Motivation Applications Image quality assessment t

48. Quality Of Lines And Edges  Inkjet Swath Alignment  2011
School: Purdue
Improved Pen Alignment for Bidirectional Printing* Edgar Bernal Prof. Jan P. Allebach Prof. Zygmunt Pizlo Purdue University * Research supported by the HewlettPackard Company. Slide No. 1 Outline Motivation: why is accurate pen alignment important? Propo

47. Quality Of Lines And Edges  Toner Scatter  2011
School: Purdue
Fundamentals HPPURDUE CONFIDENTIAL Slide No. 1 Motivation Issue Text and lines are indispensable to print quality Toner overdevelopment causes toner scatter Toner scatter makes printed pages appear blurred near the edges of text or thin lines Current

46. General Measures  Formatter Prescreening  2011
School: Purdue
An automated image prescreening tool for a printer qualification process by DuYong Ng and Jan P. Allebach Lexmark School International Inc. of Electrical and Computer Engineering, Purdue University Synopsis Anatomy of a formatterbased EP laser printer

45. General Measures  Color Image Fidelity Assessor  2011
School: Purdue
Wencheng Wu (Xerox Corporation) Zygmunt Pizlo (Purdue University) Jan P. Allebach (Purdue University) * Research supported by HP Company while Wencheng Wu was at Purdue Purdue University Page 1 Introduction Spatial color descriptor: chromatic difference S

44. General Measures  Sharpness  2011
School: Purdue
An Investigation of Perceived Sharpness and Sharpness Metrics* Buyue Zhang, Jan P. Allebach School of Electrical and Computer Engineering, Purdue University Zygmunt Pizlo Department of Psychological Sciences, Purdue University * This research is sponsored

43. IJ Printer MTF Measurement  2011
School: Purdue
Woonyoung Jang Prof. Jan P. Allebach Electronic Imaging Systems Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47907 HPPurdue Confidential Purdue University 1 What is MTF Test target page design Scanner colo

42. Optimal Unsharp Mask  2011
School: Purdue
Purdue University Research objective and motivations Optimal unsharp mask Modeling/characterization of the imaging pipeline Optimization of the filter parameters Experimental results Purdue University Conventional imaging pipeline for digital image captur

41. Image Quality Introduction  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture xx: Image Quality Image quality an illustrative example Prof. Charles A. Bouman is honored for his service as Editor of the IEEE Transactions on Image Processing (shown with Prof. Ali H. Sayed, U

40. ICC Color Management
School: Purdue
ICC color management for print production TAGA Annual Technical Conference 2002 W Craig Revie Principal Consultant Fuji Film Electronic Imaging Limited ICC Chair of the Graphic Arts Special Interest Group FujiFilm 2002 Tutorial outline About the ICC ICC

39. Device LInk Color Management
School: Purdue
Color Management Using Device Models And LookUp 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

38. Softcopy Color Banding Assessment  20011
School: Purdue
Development of Softcopy Environment for Primary Color Banding Visibility Assessment Byungseok Min*, Zygmunt Pizlo*, and Jan Allebach* *School of Electrical and Computer Engineering *Department of Psychological Sciences Purdue University, West Lafayette, I

37. Softcopy Monochrome Banding Assessment  2011
School: Purdue
Osman Arslan Prof. Zygmunt Pizlo Prof. Jan P. Allebach *This research was supported by Hewlett Packard Company. inch Paper process direction Digital value Constant image OPC drum velocity variation Image with intrinsic banding Nonuniform spacing between

36. Color Appearance Modeling  2  2011
School: Purdue

34. Gamut Mapping  2 Plus Color Appearance  2011
School: Purdue

32. Color Image Quantization  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 30: Color Image Quantization Color Image Palettization With 24 bits/pixel of video memory, color images may be displayed directly without artifacts. Many color displays have only 8 bits of video

50. Measuring Image Artifacts  Moire Artifacts  2011
School: Purdue
Outline Motivations Analytical Model of Skew Effect and its Compensation in Banding and MTF Characterization Moir Artifact Prediction and Reduction in a Variable Data Printing Environment Conclusions References Final Exam May 16th 2008 HPPURDUECONFIDENT

51. Measuring Image Artifacts  Image Resizing  2011
School: Purdue
Nearestneighbor and Bilinear Resampling Factor Estimation to Detect Blockiness or Blurriness of an Image* Ariawan Suwendi Prof. Jan P. Allebach Purdue University  West Lafayette, IN *Research supported by the HewlettPackard Company EI 2006  San Jose,

Hw1_sol
School: Purdue

Hw1
School: Purdue
Due 5:00 PM Wednesday, 2/10/99 EE 695A 1. 1.0 Homework No. 1 Spring 1998 Consider a trichromatic sensor with the sensor response functions shown below: VB() 1.0 VG() 1.0 VR() 0.4 0.5 0.6 0.7 m 0.4 0.5 0.6 0.7 m 0.4 0.5 0.6 0.7 m a. Find the response (tris

Hw4_sol
School: Purdue
ECE 638: Principles of Digital Color Imaging Systems Homework No.4 Name: Qiqi Wang Problem 1. Solution: Because the density function is symmetric to x=0 and it is a 2level quantizer, we could easy figure out the threshold should be set at 0, so we need t

Hw4
School: Purdue
Due Wednesday, 11/09/05 at 5 PM ECE 638 Homework No. 4 Fall 2005 1. Find the optimal 2level quantizer (in the sense of minimizing meansquared error for a random variable X with the following density function: 2. Consider a bichromatic (duotone) image wi

Hw3_supplement_sol
School: Purdue

Hw3_supplement
School: Purdue
Due Friday 29 April 2005 at 5:00 PM ECE 438 1. Assignment No. 10 Spring 2005 For each function given below, do the following: i. Sketch f ( x, y ) ii. Express f ( x, y ) in terms of the special functions given in class. iii. Find its CSFT F( u, v) using t

Hw3_sol
School: Purdue

Hw3
School: Purdue
Due Monday, 10/31/05 at 5 PM ECE 638 Homework No. 3 Fall 2005 1. a. Consider the signal f ( x, y) shown below which has value 1 in the shaded areas, and value 0 , elsewhere. The bars are infinitely long along the y axis; and there are infinitely many of t

Hw2_sol
School: Purdue

Hw2
School: Purdue
Due Monday, 10/3/05 at 5 PM ECE 638 1. Homework No. 2 Fall 2005 Consider a finite dimensional model for a linear, bichromatic vision system. Assume that we sample at N = 3 wavelengths. Suppose that the sensor response matrix is given by 1 0 S = 1 1 0 1

Hw1_sol
School: Purdue

Hw1
School: Purdue
Due Friday, 9/16/05 at 5 PM ECE 638 Homework No. 1 Fall 2003 1. Prove that Grassman laws are satisfied for a trichromatic sensor that obeys the model developed in class. 2. In class we defined the chromaticity diagram or Maxwells triangle, as the plane th

58. Computational Color  Estimating The Illuminant  2011
School: Purdue

57. Computational Color  Dichromatic Model  2011
School: Purdue

55. Spectral Color  Color Mouse  2011
School: Purdue
Mark Wolski*, Charles A. Bouman, Jan P. Allebach Purdue University, School of Electrical and Computer Engineering, West Lafayette, IN 47907 Eric Walowit Color Savvy Systems Inc., Springboro, OH 45066 *now with General Motors Research and Development Cente

54. Spectral Color  Imaging Colorimeter  2011
School: Purdue
Imaging Colorimetry using a Digital Camera with Dental Applications Jan P. Allebach School of Electrical and Computer Engineering Purdue University 5 November 2002 Imaging Colorimetry using a Digital Camera with Dental Applications Procter & Gamble  5 No

52. Spatiochromatic_models With SSIM  2011
School: Purdue
Spatiochromatic Vision Models for Imaging with Applications to the Development of Image Rendering Algorithms and Assessment of Image Quality Jan P. Allebach School of Electrical and Computer Engineering Purdue University West Lafayette, Indiana allebach@p

31. Basics Of 1D Signal Quantization V2  2011
School: Purdue
EE 638: Principles of Digital Color Imaging Systems Lecture 29: Basics of 1D Signal Quantization 1D Quantization Basics Review Input: Output: threshold Uniformly Quantizer I. Characterize Distortion Use MSE (Mean Squared Error) Signal Independent. for a