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