matrix resulted. All values that were not one-zero were converted to standard normal

Similar documents
Tables Table A Table B Table C Table D Table E 675

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

Topic 21 Goodness of Fit

Brightness induction: Unequal spatial integration with increments and decrements

Introduction to Statistical Data Analysis Lecture 7: The Chi-Square Distribution

4.2 The Normal Distribution. that is, a graph of the measurement looks like the familiar symmetrical, bell-shaped

Lecture 3. The Population Variance. The population variance, denoted σ 2, is the sum. of the squared deviations about the population

Continuous Distributions

Midterm Summary Fall 08. Yao Wang Polytechnic University, Brooklyn, NY 11201

Essential Statistics Chapter 6

APPENDICES APPENDIX A. STATISTICAL TABLES AND CHARTS 651 APPENDIX B. BIBLIOGRAPHY 677 APPENDIX C. ANSWERS TO SELECTED EXERCISES 679

Chapter Eight: Assessment of Relationships 1/42

Chapte The McGraw-Hill Companies, Inc. All rights reserved.

Density Curves and the Normal Distributions. Histogram: 10 groups

Exam details. Final Review Session. Things to Review

HYPOTHESIS TESTING: THE CHI-SQUARE STATISTIC

Color vision and colorimetry

CS681 Computational Colorimetry

Determining CIEDE2000 for Printing Conformance

Math 2311 Sections 4.1, 4.2 and 4.3

Chapter 1. Looking at Data

Color2Gray: Salience-Preserving Color Removal

The CIE 1997 Interim Colour Appearance Model (Simple Version), CIECAM97s. CIE TC1-34 April, 1998

Contrast gain control: a bilinear model for chromatic selectivity

CS Color. Aditi Majumder, CS 112 Slide 1

Analysis of variance (ANOVA) Comparing the means of more than two groups

Color vision and colorimetry

CIE UNIFORM CHROMATICITY SCALE DIAGRAM FOR MEASURING PERFORMANCE OF OSA-UCS EE AND CIEDE00 FORMULAS

Dover- Sherborn High School Mathematics Curriculum Probability and Statistics

Statistical Analysis for QBIC Genetics Adapted by Ellen G. Dow 2017

CHAPTER 17 CHI-SQUARE AND OTHER NONPARAMETRIC TESTS FROM: PAGANO, R. R. (2007)

Chapter 8 - Statistical intervals for a single sample

Session 3 The proportional odds model and the Mann-Whitney test

12 Chi-squared (χ 2 ) Tests for Goodness-of-fit and Independence

Contents. Acknowledgments. xix

CS 361: Probability & Statistics

Unsupervised Image Segmentation Using Comparative Reasoning and Random Walks

Color Perception: Controlled Excitation of Opponent Channels

ECE 468: Digital Image Processing. Lecture 8

ESP 178 Applied Research Methods. 2/23: Quantitative Analysis

Continuous random variables

Alternative Presentation of the Standard Normal Distribution

Practical Statistics for the Analytical Scientist Table of Contents

Formulas and Tables. for Essentials of Statistics, by Mario F. Triola 2002 by Addison-Wesley. ˆp E p ˆp E Proportion.

DETAILED CONTENTS PART I INTRODUCTION AND DESCRIPTIVE STATISTICS. 1. Introduction to Statistics

DEM A TMH-PW-N

Application of Variance Homogeneity Tests Under Violation of Normality Assumption

SPSS LAB FILE 1

Inter-Rater Agreement

MULTIPLE LINEAR REGRESSION IN MINITAB

Basic Business Statistics, 10/e

Technical Data Sheet 5.0 mm Round LED (T-1 3/4 )

CIVL /8904 T R A F F I C F L O W T H E O R Y L E C T U R E - 8

Formulas and Tables by Mario F. Triola

Data are sometimes not compatible with the assumptions of parametric statistical tests (i.e. t-test, regression, ANOVA)

Motion influences the Effects of Systematic Chromatic Changes

Statistics Handbook. All statistical tables were computed by the author.

Statistical Inference Theory Lesson 46 Non-parametric Statistics

1 Introduction to Minitab

Glossary. The ISI glossary of statistical terms provides definitions in a number of different languages:

Supplement of Vegetation greenness and land carbon-flux anomalies associated with climate variations: a focus on the year 2015

Lecture 41 Sections Mon, Apr 7, 2008

Introduction to Colorimetry

Supplemental Data: Appendix

Lightness, equivalent backgrounds, and anchoring

Lecture Slides. Elementary Statistics. by Mario F. Triola. and the Triola Statistics Series

Continuous Random Variables. and Probability Distributions. Continuous Random Variables and Probability Distributions ( ) ( ) Chapter 4 4.

Lecture Slides. Section 13-1 Overview. Elementary Statistics Tenth Edition. Chapter 13 Nonparametric Statistics. by Mario F.

DEM C TTH-PW-N

68% 95% 99.7% x x 1 σ. x 1 2σ. x 1 3σ. Find a normal probability

E/ECE/324/Rev.2/Add.127/Amend.7 E/ECE/TRANS/505/Rev.2/Add.127/Amend.7

Ø Set of mutually exclusive categories. Ø Classify or categorize subject. Ø No meaningful order to categorization.

Digital Image Processing

In this investigation you will use the statistics skills that you learned the to display and analyze a cup of peanut M&Ms.

Mass measurements at the Large Hadron Collider

psychological statistics

Statistical Intervals (One sample) (Chs )

FREQUENCY DISTRIBUTIONS AND PERCENTILES

Non-parametric (Distribution-free) approaches p188 CN

CHAPTER 7 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS. From Exploratory Factor Analysis Ledyard R Tucker and Robert C. MacCallum

SUPPLEMENTARY INFORMATION

Technical Data Sheet 3 mm Round LED (T-1)

6.1 Frequency Distributions from Data. Discrete Distributions Example, Four Forms, Four Uses Continuous Distributions. Example, Four Forms, Four Uses

Continuous Random Variables. and Probability Distributions. Continuous Random Variables and Probability Distributions ( ) ( )

Psych 230. Psychological Measurement and Statistics

How many states. Record high temperature

of the Guanine Nucleotide Exchange Factor FARP2

Calculating Effect-Sizes. David B. Wilson, PhD George Mason University

Analysis of Variance: Part 1

PS2.1 & 2.2: Linear Correlations PS2: Bivariate Statistics

Stat 101 Exam 1 Important Formulas and Concepts 1

Ch. 7. One sample hypothesis tests for µ and σ

The Chi-Square Distributions

Find the component form of with initial point A(1, 3) and terminal point B(1, 3). Component form = 1 1, 3 ( 3) (x 1., y 1. ) = (1, 3) = 0, 6 Subtract.

SPEC NO: DSAJ6327 REV NO: V.1 DATE: JUL/31/2009 PAGE: 1 OF 8 APPROVED: WYNEC CHECKED:

Lecture 9. Selected material from: Ch. 12 The analysis of categorical data and goodness of fit tests

The CIECAM02 color appearance model

Vorlesung 2. Visualisation of wave functions (April 18, 2008)

Background to Statistics

Technical Data Sheet 3 mm Round LED (T-1)

Transcription:

Appendix A A. General Statistics A.1 Morrisey s Incomplete Matrix Solution for Case V Because there was unanimous agreement for some pairs, a zero-one proportion matrix resulted. All values that were not one-zero were converted to standard normal deviates and the scale values were solved using Morrisey s incomplete matrix solution. The text below is based on the description given by Engeldrum in, Psychometric Scaling: A Toolkit for Imaging Systems Development (2, pg 117). The column vector, z, contains all the z-score values excluding the incomplete proportions. Matrix X is formed such that the columns correspond to the samples and the rows represent the judged pair. Note that for an incomplete matrix there are (k+1)n rows, where k is less than n(n-1)/2. The entries of X consisted of +1 and -1 in the columns of the pair that were compared (pairs that did not produce zero-one proportions). An n by 1 column forms the S vector, which represents the unknown scale values. The rank of the X matrix is increased by adding the constraint that the sum of the scale values equals zero. Thus, an extra row of 1 sis added as the final row in the X matrix, and a added as the last element of vector Z. The final matrix formulation is illustrated in equation (A.1). The least squares solution, equation (A.2), is used to solve for S. (A.1) z z z M 12 13 24 1 1 = M 1 1 1 M 1 1 M 1 1 S S S S S k + 1 n n M 1 L L L M L M 1 n n n 1 2 3 4 (A.2) S = (X' X) 1 X' z 16

A.2 Average Absolute Deviation (AAD) and χ 2 Goodness-of-fit The goodness-of-fit of the paired comparison and rank order data was tested using both the average absolute deviation (AAD) and Mosteller s χ 2 Test. First, the difference of the scale value pairs, Si-Sj, was computed and the result transformed to predicted probabilities (p ) using the standard normal cumulative distribution function. Note, these proportions are what is expected if the Case V model is correct. The proportions obtained experimentally (p) can be compared to the predicted proportions (p ) by computing the average absolute deviation as shown in equation (A.3). The results from this equation indicate the percent difference between the observed and predicted data. (A.3) p' p 2 = n(n 1) i> j p' p, where p' = predicted proportion p = observed proportions n = number of stimuli. from results, from the data, and The chi-square test is computed on the arcsine transformation of the matrix of predicted proportions (p ) and observed proportions (p) as suggested by Mosteller (1951) and given in equations (A.4) and (A.5). 1 (A.4) θ' = sin 1 ( 2 p' 1) in rad θ = sin ( 2 p 1) in rad (A.5) χ 2 = J i> j ( θ' θ ), where J = number of observers with (n -1)(n - 2)/2 degrees of freedom 2 161

A.3 Supplement to Table 5.3 Table A.1 Goodness-of-fit measured for Paired Comparison Case V solution wakeboarder vegetables firefighters kids bug Pioneer Plasma χ 2 AAD χ 2 AAD χ 2 AAD χ 2 AAD χ 2 AAD Rank Order 8.15.58 12.74.4 17.326.83 1.667.48 16.28.819 Paired Comparison 18.93.63 21.957.53 6.1562.51 19.452.72 89.82.1396 wakeboarder vegetables firefighters kids bug Apple Cinema χ 2 AAD χ 2 AAD χ 2 AAD χ 2 AAD χ 2 AAD Rank Order 7.84.53 3.957.45 1.125.7 15.425.69 19.73.863 Paired Comparison 19.69.95 2.891.93 9.2618.61 36.858.74 5.18.1116 Critical value χ 2 (α =.95; df = 1) = 18.31 where P{ χ 2 (v) 18.31). Poor fits are indicated by bold type in the table. 162

Appendix B B. Supplementary u v Chromaticity Plots (Chapter 6) B.1 Achromatic Patch Adjustment.49 D65 vs. D93.48.47.46.45.44.43.42 D65 D93 Mean D65 Mean D93.41.16.17.18.19.2.21.22 Figure B6.6 Plots subject s final patch adjustments for D65 and D93 white point images. The black marker represents the mean D65 u v coordinates and the green marker represents the mean D93 u v. 163

Normal Image D65 White Point Normal Image D93 White Point.48.47.46.45.44.43.42.41.16.18.2.22 Mosaic Image D65 White Point.48.47.46.45.44.43.42.41.16.18.2.22 Mosaic Image D93 White Point data D93 wtpt D65 wtpt Mean.48.47.46.45.44.43.42.41.16.18.2.22 Gray Image D65 White Point.48.47.46.45.44.43.42.41.16.18.2.22 Gray Image D93 White Point.48.47.46.45.44.43.42.41.16.18.2.22.48.47.46.45.44.43.42.41.16.18.2.22 Figure B6.7 Plots subject s final patch adjustments for N, M, and G images groups. The black marker represents the D65 u v white point and the cyan marker represents D93 white point. The green marker represents the mean u v for the data in each plot. 164

.5 3faces.5 auto.48.48.46.44.46.44 data mean image (D65) mean image (D93) D65 wtpt D93 wtpt.42.16.18.2.22.24.42.16.18.2.22.24.5 botonists.5 business.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24.5 chemist.5 graymushrooms.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24 165

.5 livestock.5 lunch.48.48.46.44.42.16.18.2.22.24.46.44.42.16.18.2.22.24 data mean image (D65) mean image (D93) D65 wtpt D93 wtpt.5 scooter.5 smoke.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24.5 watermellon.5 worker.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24 Figure B6.8 & B6.9 Patch adjustments separated across individual images. Red markers represent mean a* b* of the image with a D65 white point, and green markers represents the mean a* b* of the image with the D65 white point. The black and blue markers indicate the D65 and D93 true white points as a reference. 166

.5 botonists N.tif.5 botonists N D93.tif.48 left face right face.48 left face.46.46 right face.44.44.42.16.18.2.22.24.42.16.18.2.22.24 business N.tif.5 left face right face.48.46.5.48.46 business N D93.tif left face right face.44.42 wall&text.44.42.4.16.18.2.22.24.4 wall&text.16.18.2.22.24.5 smoke N.tif.5 smoke N D93.tif.48 right face left face.48.46.46 right face left face.44.44.42.16.18.2.22.24.42.16.18.2.22.24 Figure B6.13 Mean u v data extracted from areas that received the most is indicated by the cyan, magenta, and yellow (for the business image) makers. Red makers indicate the mean u v of the image, and black and blue markers plot the white points of the image as references. 167

B.2 Selecting the Most Achromatic Region.49.48 D65 vs. D93 D65 D93 Mean D65 Mean D93.47.46.45.44.43.42.41.16.17.18.19.2.21.22 Figure B6.16 Plots subjects achromatic selections for D65 and D93 white point images. The black marker represents the mean D65 u v coordinates and the green marker represents the mean D93 u v. 168

25 2 frequency 15 1 5 5 1 15 2 25 3 35 4 45 Y (cd/m2) Figure B6.17 Histogram of luminance values from the achromatic selection task across all images. 169

.5 3faces.5 auto.48.48.46.44.42.16.18.2.22.24.5 botanists.46.44.42.16.18.2.22.24.5 business data mean image (D65) mean image (D93) D65 wtpt D93 wtpt.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24.5 chemist.5 graymushrooms.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24 17

.5 livestock.5 lunch.48.46.48.46 data mean image (D65) mean image (D93) D65 wtpt D93 wtpt.44.44.42.16.18.2.22.24.5 scooter.42.16.18.2.22.24.5 smoke.48.48.46.46.44.44.42.16.18.2.22.24.5 watermelon.42.16.18.2.22.24.5 worker.48.48.46.46.44.44.42.16.18.2.22.24.42.16.18.2.22.24 Figure B6.18 Achromatic selection data separated across individual images. Red markers represent mean a* b* of the image with a D65 white point, and green markers represent the mean a* b* of the images with the D65 white point. The black and blue markers indicate the D65 and D93 true white points as a reference. 171

Appendix C C. Supplementary Manipulation Plots (Chapter 5) 25.. 2. apple median L* plasma median L* manipulated - orignal (L*) 15. 1. 5.. -5. -1. wake1 wake3 wake5 wake4 wake6 veggies1 veggies3 veggies5 veggies6 veggies4 fire2 fire3 fire4 fire5 fire6 kids1 kids2 kids4 kids5 kids6 bug1 bug2 bug4 bug5 bug6 25. 2. apple median h plasma median h manipulated - orignal (hue) 15. 1. 5.. -5. -1. wake1 wake3 wake5 wake4 wake6 veggies1 veggies3 veggies5 veggies6 veggies4 fire2 fire3 fire4 fire5 fire6 kids1 kids2 kids4 kids5 kids6 bug1 bug2 bug4 bug5 bug6 172

25. 2. apple median C* plasma median C* 15. 1. 5.. -5. -1. 35. 3. apple median E94 plasma median E94 manipulated - orignal (DE94) 25. 2. 15. 1. 5.. wake1 wake3 wake5 wake4 wake6 veggies1 veggies3 veggies5 veggies6 veggies4 fire2 fire3 fire4 fire5 fire6 kids1 kids2 kids4 kids5 kids6 bug1 bug2 bug4 bug5 bug6 wake1 wake3 wake5 wake4 wake6 veggies1 veggies3 veggies5 veggies6 veggies4 manipulated - orignal (C*) fire2 fire3 fire4 fire5 fire6 kids1 kids2 kids4 kids5 kids6 bug1 bug2 bug4 bug5 bug6 Figure C Graphs show the median pixel differences from the original image in CIE lightness (L* ab ), chroma (C* ab ), and hue (h ab ) coordinates using the forward models of the two displays. The median color differences (DE94) are also shown as a reference. 173