1 Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX 78712-1084 USA 1 Mr. Vishal Monga,

Slides:



Advertisements
Similar presentations
Filtration based on Color distance
Advertisements

Digital Image Processing Lecture 3: Image Display & Enhancement
Contrast-Aware Halftoning Hua Li and David Mould April 22,
Spatial Filtering (Chapter 3)
 Image Characteristics  Image Digitization Spatial domain Intensity domain 1.
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
School of Computing Science Simon Fraser University
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.
Introduction to Image Quality Assessment
Half Toning. Continuous Half Toning Color Half Toning.
Color Fidelity in Multimedia H. J. Trussell Dept. of Electrical and Computer Engineering North Carolina State University Raleigh, NC
Perceived video quality measurement Muhammad Saqib Ilyas CS 584 Spring 2005.
Image Compression - JPEG. Video Compression MPEG –Audio compression Lossy / perceptually lossless / lossless 3 layers Models based on speech generation.
Introduction to electrical and computer engineering Jan P. Allebach School of Electrical and Computer Engineering
Digital Halftoning.
Introduction to JPEG Alireza Shafaei ( ) Fall 2005.
IDL GUI for Digital Halftoning Final Project for SIMG-726 Computing For Imaging Science Changmeng Liu
How to Make Printed and Displayed Images Have High Visual Quality
INTERPOLATED HALFTONING, REHALFTONING, AND HALFTONE COMPRESSION Prof. Brian L. Evans Collaboration.
Dr. Niranjan Damera-Venkata (HP Labs) Dr. Thomas D. Kite (Audio Precision) Ph.D. Graduates: Dr. Niranjan Damera-Venkata (HP Labs) Dr. Thomas D. Kite (Audio.
COLOR HISTOGRAM AND DISCRETE COSINE TRANSFORM FOR COLOR IMAGE RETRIEVAL Presented by 2006/8.
Purdue University Page 1 Color Image Fidelity Assessor Color Image Fidelity Assessor * Wencheng Wu (Xerox Corporation) Zygmunt Pizlo (Purdue University)
HP-PURDUE-CONFIDENTIAL Final Exam May 16th 2008 Slide No.1 Outline Motivations Analytical Model of Skew Effect and its Compensation in Banding and MTF.
03/05/03© 2003 University of Wisconsin Last Time Tone Reproduction If you don’t use perceptual info, some people call it contrast reduction.
Image Filtering Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 02/02/10.
Digital Image Processing Lecture 3: Image Display & Enhancement March 2, 2005 Prof. Charlene Tsai.
EE445S Real-Time Digital Signal Processing Lab Spring 2014 Lecture 10 Data Conversion Slides by Prof. Brian L. Evans, Dept. of ECE, UT Austin, and Dr.
Dr. Niranjan Damera-Venkata (HP Labs) Dr. Thomas D. Kite (Audio Precision) Dr. Vishal Monga (Xerox Labs) Ph.D. Graduates: Dr. Niranjan Damera-Venkata (HP.
PS221 project : pattern sensitivity and image compression Eric Setton - Winter 2002 PS221 Project Presentation Pattern Sensitivity and Image Compression.
Image Coloring. Halftone Halftone is the reprographic technique that simulates continuous tone imagery through the use of dots, varying either in size,
Just Noticeable Difference Estimation For Images with Structural Uncertainty WU Jinjian Xidian University.
1 Research Question  Can a vision-based mobile robot  with limited computation and memory,  and rapidly varying camera positions,  operate autonomously.
Halftoning With Pre- Computed Maps Objective Image Quality Measures Halftoning and Objective Quality Measures for Halftoned Images.
AM-FM Screen Design Using Donut Filters
02/05/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Mach Banding –Humans exaggerate sharp boundaries, but not fuzzy ones.
LUT Method For Inverse Halftone 資工四 林丞蔚 林耿賢. Outline Introduction Methods for Halftoning LUT Inverse Halftone Tree Structured LUT Conclusion.
Demosaicking for Multispectral Filter Array (MSFA)
3-1 Chapter 3: Image Display The goodness of display of an image depends on (a) Image quality: i) Spatial resolution, ii) Quantization (b) Display device:
Halftoning-Inspired Methods for Foveation in Variable Acuity Superpixel Imager Cameras Thayne R. Coffman 1,2 Prof. Brian L. Evans 1 (presenting) Prof.
CDS 301 Fall, 2008 Image Visualization Chap. 9 November 11, 2008 Jie Zhang Copyright ©
Error Diffusion (ED) Li Yang Campus Norrköping (ITN), University of Linköping.
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.
1 of 32 Computer Graphics Color. 2 of 32 Basics Of Color elements of color:
Spatial Filtering (Chapter 3) CS474/674 - Prof. Bebis.
Tone Dependent Color Error Diffusion Halftoning
Prof. Brian L. Evans Embedded Signal Processing Laboratory
2.1 Direct Binary Search (DBS)
1.3 Error Diffusion – Basic Concepts
Lossy Compression of Stochastic Halftones with JBIG2
Tone Dependent Color Error Diffusion
Multi-Class Error-Diffusion with Blue-Noise Property
Prof. Brian L. Evans Embedded Signal Processing Laboratory
Variations on Error Diffusion: Retrospectives and Future Trends
Spatiochromatic Vision Models for Imaging
The Chinese University of Hong Kong
School of Electrical and
School of Electrical and
FM Halftoning Via Block Error Diffusion
Color Error Diffusion with Generalized Optimum Noise Shaping
Data Conversion Slides by Prof. Brian L. Evans, Dept. of ECE, UT Austin, and Dr. Thomas D. Kite, Audio Precision, Beaverton, OR
Tone Dependent Color Error Diffusion
1.2 Design of Periodic, Clustered-Dot Screens
DIGITAL HALFTONING Sasan Gooran.
A Review in Quality Measures for Halftoned Images
3.3 Screening Part 3.
2.2 Design of Aperiodic, Dispersed-Dot Screens
Tone Dependent Color Error Diffusion Halftoning
Gradient Domain Salience-preserving Color-to-gray Conversion
DIGITAL IMAGE PROCESSING Elective 3 (5th Sem.)
Presentation transcript:

1 Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX USA 1 Mr. Vishal Monga, 2 Dr. Niranjan Damera-Venkata and 1 Prof. Brian L. Evans An Input-Level Dependent Approach To Color Error Diffusion 2 Hewlett-Packard Laboratories 1501 Page Mill Road Palo Alto, CA USA SPIE/IS&T Symposium on Electronic Imaging

2 current pixel weights 3/16 7/16 5/161/16 + _ _ + e(m)e(m) b(m)b(m)x(m)x(m) differencethreshold compute error shape error u(m)u(m) Error Diffusion Spectrum Grayscale Error Diffusion Halftoning 2- D sigma delta modulation [Anastassiou, 1989] –Shape quantization noise into high freq. Several Enhancements –Variable thresholds, weights and scan paths Background

3 - Computationally too expensive for real-time applications e.g. printing - Used in screen design - Practical upper bound for achievable halftone quality Background Direct Binary Search [Analoui, Allebach 1992]

4 Tone Dependent Error Diffusion Train error diffusion weights and threshold modulation [Li & Allebach, 2002] b(m)b(m) + _ _ + e(m)e(m) x(m)x(m) Tone dependent error filter Tone dependent threshold modulation Graylevel patch x Halftone pattern for graylevel x FFT DBS pattern for graylevel x Halftone pattern for graylevel x FFT Midtone regions Highlights and shadows Grayscale TDED

5 Input-Level Dependent Color Error Diffusion Extend TDED to color? –Goal: e.g. for RGB images obtain optimal (in visual quality) error filters with filter weights dependent on input RGB triplet (or 3-tuple) –Applying grayscale TDED independently to the 3 (or 4) color channels ignores the correlation amongst them Processing: channel-separable or vectorized –Error filters for each color channel (e.g. R, G, B) –Matrix valued error filters [Damera-Venkata, Evans 2001] Design of error filter key to quality –Take human visual system (HVS) response into account Color TDED

6 Problem(s): –(256) 3 possible input RGB tuples –Criterion for error filter design? Solution –Design error filters along the diagonal line of the color cube i.e. (R,G,B) = {(0,0,0) ; (1,1,1) …(255,255,255)} –256 error filters for each of the 3 color planes –Color screens are designed in this manner –Train error filters to minimize the visually weighted squared error between the magnitude spectra of a “constant” RGB image and its halftone pattern Input-Level Dependent Color Error Diffusion Color TDED

7 C1C1 C2C2 C3C3 Perceptual color space Spatial filtering Perceptual Model [Poirson, Wandell 1997] Separate image into channels/visual pathways –Pixel based transformation of RGB  Linearized CIELab –Spatial filtering based on HVS characteristics & color space Color HVS Model

8 Linearized CIELab Color Space Linearize CIELab space about D65 white point [Flohr, Kolpatzik, R.Balasubramanian, Carrara, Bouman, Allebach, 1993] Y y = 116 Y/Yn – 116 L = 116 f (Y/Yn) – 116 C x = 200[X/Xn – Y/Yn] a* = 200[ f(X/Xn ) – f(Y/Yn ) ] C z = 500 [Y/Yn – Z/Zn] b* = 500 [ f(Y/Yn ) – f(Z/Zn ) ] where f(x) = 7.787x + 16/116 0 ≤ x < f(x) = x 1/ ≤ x ≤ 1 Color Transformation –sRGB  CIEXYZ  Y y C x C z –sRGB  CIEXYZ obtained from Color TDED

9 HVS Filtering Filter chrominance channels more aggressively –Luminance frequency response [Näsänen and Sullivan, 1984] L average luminance of display weighted radial spatial frequency –Chrominance frequency response [Kolpatzik and Bouman, 1992] –Chrominance response allows more low frequency chromatic error not to be perceived vs. luminance response Color TDED

10 Color Transformation sRGB  Y y C x C z (Linearized CIELab) FFT Input RGB Patch Halftone Pattern  Perceptual Error Metric Color TDED

11 HVS Chrominance Frequency Response HVS Luminance Frequency Response HVS Chrominance Frequency Response Total Squared Error (TSE)  YyYy CxCx CzCz Find error filters that minimize TSE subject to diffusion and non-negativity constraints, m = r, g, b; a  (0, 255) (Floyd-Steinberg) Perceptual Error Metric Color TDED

12 Results Color TDED (a) Original Color Ramp Image (b) Floyd-Steinberg Error Diffusion

13 Results … Color TDED (c) Separable application of grayscale TDED (d) Color TDED

14 Results … Color TDED Halftone Detail – Blue section of the color ramp Floyd-Steinberg Grayscale TDEDColor TDED

15 Original House Image

16 Floyd Steinberg Halftone

17 Color TDED Halftone

18 Color TDED –Worms and other directional artifacts removed –False textures eliminated –Visibility of “halftone-pattern” minimized (HVS model) –More accurate color rendering (than separable application) Future Work –Incorporate Color DBS in error filter design to enhance homogenity of halftone textures –Design visually optimum matrix valued filters Conclusion & Future Work Color TDED

Back Up Slides

20 Floyd Steinberg Y y component

21 Floyd Steinberg C x component

22 TDED Y y component

23 TDED C x component

24 where p = (u 2 +v 2 ) 1/2 and w – symmetry parameter reduces contrast sensitivity at odd multiples of 45 degrees Role of frequency weighting – weighting by a function of angular spatial frequency [Sullivan, Ray, Miller 1991] equivalent to dumping the luminance error across the diagonals where the eye is least sensitive. HVS Filtering contd… Color TDED