COLOR BLIND FILTERS
By Stacey Osborn
OUTLINE
• INTRODUCTION
• PREVIOUS WORK
• DESCRIPTION
• EXPERIMENTS
• RESULTS
• DISCUSSION
INTRODUCTION
Color Blindness or Color Deficiency affects
approximately 8% of men and 0.5% of women of
European descent. It can be broken down in to three
main categories: Monochromacy, Dichromacy and
Anomalous Trichromacy. Dichromacy can be further
broken down in to Protanopia, Deuteranopia, and
Tritanopia.
In this project I focused primarily on the Deuteranopia
form of Dichromacy.
INTRODUCTION – CONTINUED
The purpose of this project was to manipulate
digital images using several different
processing techniques including: a
Daltonization Method, a Recoloring Method, A
Contrast Adjustment Method, and a LAB
Contrast Adjustment Method in order to make
them viewable and distinguishable to those
with color vision deficiency and determine
which method produces the clearest result.
PREVIOUS WORK
•
•
•
•
•
•
•
•
Daltonization – Daltonize.org
Chrome Daltonize Plugin
Colorblind Glasses / Contacts
Smartphone Real Time Filter Applications
Image Recoloring Techniques
Contrast Adjustments
Color Blind Simulators for Developers
Color Blind Friendly Colormaps for Developers
DESCRIPTION - DALTONIZATION
• The Daltonization method is currently the
most accepted image manipulation
algorithm and has many variations and
improvements and is used in several browser
plugins.For this project I used the very simple
version from daltonize.org
The first step is to find the LMS values of the RGB image using a conversion
matrix
• Then another conversion is made to delete the information associated
with the loss of any of the cone types
• Finally the activity is converted back to the RGB color space
•
DESCRIPTION - RECOLORING
• Recoloring builds on the
Daltonization approach
requiring more complex
algorithms which have
been the focus of several
recent academic studies
• Mass Spring Method
• Rasche Method
• Generalized Histogram
Equalization
DESCRIPTION – RECOLORING CONTINUED
• All recoloring methods have the same main
goals – Preserving Contrast and Maintaining
Luminance
• The Mass Spring Method used in my survey uses
three steps:
• Image Quantization
• Mass Spring Optimization
• Reconstruction
DESCRIPTION – RECOLORING CONTINUED
• The Mass Spring Method represents the color gamut of each class of
Dichromacy with two half- planes in the LMS color space approximated
together to from a single plane passing through the luminance axis
• The luminance axis is determined by mapping the color gamut of each
class of Dichromacy to the approximately perceptually-uniform LAB
color space and taking the least-squares to obtain a plain that contains
the luminance axis
DESCRIPTION – RECOLORING CONTINUED
• Quantization is completed by assigning each quantized color Pi with
mass mi and the position of Pi is initialized with the value of the color as
seen by a Dichromat after the rotation of the luminance plane
• Each pair of particles is connected with a spring S with an elasticity
coefficient k and rest length (quantized length). At each optimization
step the positions are updated and the Spring and length are
recalculated with the goal that the perceptual distance between all
pairs and the quantized color distance will be approximately the same
• The transformed colors are then determined by inversing the rotation of
the luminance plane
DESCRIPTION – RECOLORING CONTINUED
• To adjust for the local minima problem, they switch the sign of
the b* coordinate of all rotated quantized colors whose a*
coordinates are positive with a perceptual distances between
the color itself and the color as perceived by a dichromat is
greater than a certain threshold in order to:
• Avoid ambiguity
• Switching the b* coordinate for some colors compresses and
stretches their associated spring adding additional potential energy
DESCRIPTION - CONTRAST
• First the algorithm adjusts an image’s RGB values to
enhance contrast between red and green
• The pixel values are then cut in half to make room
for pixel values to be increased
• Then for every pixel:
• Reds and Greens further from pure red or green are
increased significantly while those closer are only
marginally increased
• For pixels that are mostly red, the blue value is
reduced
• For pixels mostly green, the blue value is increased
DESCRIPTION – LAB COLOR CORRECTION
• Similar to the RGB Contrast but performed in the LAB color
space, first the image is converted from the RGB color
space to the LAB color space
• The a* values are adjusted relative to their maxima making
positive numbers more positive and negative numbers more
negative
• The b* value is adjusted based on how red or green it is to
bring out blue and yellow hues
• The l* value is then adjusted relative to the a* to adjust the
brightness
• Finally the image is converted back to the RGB color space
EXPERIMENTS
I created an online survey using Survey Monkey to
compare the Daltonization Method, a Recoloring
Method, A Contrast Adjustment Method, and a LAB
Contrast Adjustment Method. I used 6 image
categories with 5 images each and ran the results
through the Vischeck program to simulate a
Deuteranope color deficiency.
https://www.surveymonkey.com/s/9FJJM8D
Daltonization
EXPERIMENTS – ART
Recoloring
LAB Contrast
Contrast
EXPERIMENTS – TEST
LAB Contrast
Daltonization
Recoloring
Contrast
EXPERIMENTS - FLOWERS
LAB Contrast
Daltonization
Recoloring
Contrast
Daltonization
EXPERIMENTS - LANDSCAPES
Recoloring
LAB Contrast
Contrast
EXPERIMENTS – PEOPLE
Daltonization
Recoloring
LAB Contrast
Contrast
EXPERIMENTS – MAPS / CHARTS
Daltonization
LAB Contrast
Recoloring
Contrast
RESULTS
Daltonization
MS Recoloring
LAB Contrast
Contrast
Art
69
98
51
47
Color Blind Test
29
90
31
115
Flowers
39
176
31
19
Landscapes
84
110
28
43
People
52
148
31
34
Maps and Charts
90
108
45
22
363
730
217
280
Survey was completed online using Survey Monkey with 53 respondents; the Mass Spring
Recoloring method was preferred by a large margin over the other approaches examined.
DISCUSSION
Why Does this Matter to Future Developers?
QUESTIONS ?