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CSE 564: Visualization
Color
Klaus Mueller
Computer Science Department
Stony Brook University
Perception of Light Intensity
How Many Intensity Levels Do We Need? Dynamic Intensity Range Issues
Dynamic range of the natural world:
100 000 000:1
Dynamic range the eye can accommodate in a single view:
10 000:1
Dynamic range a typical monitor can display:
100:1
Dynamic range a typical camera can capture:
100:1
Long Camera Exposure
captured the interior well, but the outside is too bright…
Short Camera Exposure
captured the outside well, but now the interior is dark…
Medium Camera Exposure
everything is somewhat present, but not very detailed
What Now…
OK.. now we have three images that have captured all the detail of the scene• but we want to visualize it all in one picture, not three• we need some way to merge these three pictures• this is the domain of High Dynamic Range Imaging (HDR)
How does HDR work?
Two methods:• somehow compress the large range into a small, displayable range• look at small neighborhoods and try to maximize contrast in each• the first is a global method, the second is a local method
This is also often called tone mapping
Another application of HDR:• computational datasets are often computed in floating point precision• HDR can be used to compress the floating point images into 8-bit
Methods
Global methods: • scale each pixel according to a fixed curve• the key issue is here: the shape of the curve
Local methods: • group small neighborhoods by their average value• scale these averages down• add detail back in
Methods
Local methods: • group small neighborhoods by their average value• scale these averages down• add detail back in
local smoothing
scale downadd detail
back in
before
after
+
Comparison: Global Method Comparison: Local Method
Result With Earlier Example Example: Grand Canal
Example: Grand Canal Example: Grand Canal
Example: Grand Canal Example: Foyer
Example: Foyer Example: Foyer
Example: Foyer References
HDR has become a popular technique
Some of the key HDR researchers are:• P. Debevec, E. Reinhard, G. Ward, M. Ashikhmin, J. Tumblin, and
others• for use of HDR in scientific visualization, see X. Yuan, M. Nguyen, B.
Chen and D. Porter, “High Dynamic Range Volume Visualization,” IEEE Transaction on Visualization and Computer Graphics, vol. 12, no. 4, 2006.
Image examples were taken from http://www.hdrsoft.com
Back to The Optical Illusion Example Explanation
While the retina can perceive a high range of intensities, it cannot handle all simultaneously• at any given time, each region adapts to a small intensity range
determined by the local intensity• that is why you have to wait a while when you step from a bright into
a dark room (say, a dark movie theater from a brightly lit lobby)
current dark area in picture falls here
current adapted range
after moving the eye:new bright area saturates
intensity perception
after moving the eye:eventually adapted
range
eventually the bright area intensity is unsaturated, matches neighborhood
(which was already adapted here before)
Spectrum of Wavelengths Perception Curves
human color sensitivity curvescolor generation with primaries
Perceptional Color Spaces Use Of The CIE Chromaticity Diagram
The Munsell Perceptional Color Space
The (irregularly shaped) Munsell tree has 3 axes:• chroma (saturation): distance from the core (values 0-30, with
fluorescent colors having the maximum 30)• value (brightness): vertical axis (0– 10 (white))• hue: 10 principal hues (R, YR, Y, GY, G, BG,
B, PB, P, RP)
Non-Perceptional Color Spaces
RGBHSV
compare to: CIE LAB in 3D
blue
red
green
white
magenta
yellow
cyan
Application: Colorization of Grey-Level Images Application: Colorization of Grey-Level Images
movie:
Application: Colorization of Grey-Level Images
movie:
References
More information:• T. Welsh, M. Ashikhmin, and K. Mueller, "Transferring color to
greyscale images," ACM Transactions on Graphics (Proc. of SIGGRAPH'02), vol. 21, no. 3, pp. 277-280, 2002.
More on Color Labeling vs. Contrast
from: M. Stone
More on Color More on Color
Use of Color Luminance Contrast
Luminance Contrast Color Contrast and Harmony
Color Harmony
Non-harmonic colors Harmonic colors
Hue wheel:
Harmonic Color Schemes
i type V type L type I type
T type Y type X type N type
Color Harmonization Procedure (1)
Given arbitrary hue histogram H(p) for image X, find the closest harmonic template Tm • minimize the distance of the histogram to template coverage
(delineated by template edges E)• use an optimization procedure for this• also find the orientation angle α
)(||)()(||)),(( )( pSpEpHmXFXp
Tm⋅−=
∈αα
non-harmonic harmonized
from Cohen ‘08
Color Harmonization Procedure (2)
Given closest template and α has been found (user may specify other template)• shift all hues H(p) to the closest harmonic template position H’(p) with
width w• a Gaussian G controls the clustering of the hues around the sector
mean C of the template (greater σ clusters more, we use w/2)
This may break up coherent regions into disjointly colored regions• to avoid this, may embed a graph-cut s based
labeling into the shifting procedure
||))()((||1(2
)()(' pCpHGwpGpH −−+= σ
from Cohen ‘08
Color Harmonization: Example
Collage harmonization (from Cohen ’06):
non-harmonic harmonized (T type)
Color Harmonization: Example
Collage harmonization (from Wang ‘08):
Color Constancy
A psychophysical phenomenon:• accounts for the ability of humans to accurately perceive the "color" of
an object under different lighting conditions • lighting, or illumination, may vary both over a viewed scene and over
time yet the perceived color is constant• in fact, constant illumination over a scene is almost never
encountered in real life
Given an object, the colors we perceive (within limits) remain the same, even though… • the spectral content ("color") of sunlight varies greatly through the day
and with weather conditions• artificial light sources also vary greatly from
one to another
Color Constancy: Example
illuminant A illuminant B illuminant C
Chromatic Aberration
from: J. Döllner, U Potsdam
Why Color? … Color Adds More Dimensions
from: M. Stone
Color Adds Aesthetics
from: M. Stone
But… Mapping to Color Can Cause Problems
from: M. Stone
Color Maps
from: Rogowitz/Treinish
Color Map: Segmentation Tasks
from: Rogowitz/Treinish
Color Map: Rainbow
from: Rogowitz/Treinish
Color Map: Linear Hue
from: Rogowitz/Treinish
Color Maps: Spatial Frequency Issues
from: Rogowitz/Treinish
Color Maps: Low vs. High Frequency
weather modellow frequency
radar scanhigh frequency
from: Rogowitz/Treinish
Color Maps: Highlighting
from: Rogowitz/Treinish
Brewer Scale
Nominal scales• distinct hues, but similar emphasis
Sequential scales• vary in lightness and saturation• vary slightly in hue
Diverging scale• complementary sequential scales• neutral at “zero”
from: M. Stone (see also colorbrewer.org)
Brewer Scales
from: M. Stone (see also colorbrewer.org)
Example for Proper Use of Color
References
Maureen Stone, A Field Guide to Digital Color, AK Peters 2003• color perception and design with color
Colin Ware, Perception and Information Visualization: 2nd
Edition, Morgan Kaufman, 2004• book specifically geared towards information visualization
Bernice Rogowitz and Lloyd Treinish, “An architecture for perceptual rule-based visualization,” Proc. IEEE Visualization 1993, pp. 236-243, 1993• see also
http://www.research.ibm.com/dx/proceedings/pravda/index.htmhttp://www.research.ibm.com/dx/proceedings/pravda/truevis.htm
Color brewer: http://www.colorbrewer.org