Lightness, Brightness and Contrast

Week 3 :CCT370 – Introduction to Computer Visualization

The Big Picture (again) Ecological optics/perception

Gibson Perception is in service of

action For evolutionary (survival)

advantage See/perceive things that allow

action E.g., surfaces for walking on,

objects for interacting with, …

Leads to (visual) system that: Does extract “elementary”

elements to use in perception Features Stage 1 Basis of sensory systems

AND interaction throughout system leads to perception Stages 2 and 3

Unfortunately … This evolutionarily derived system has pitfalls

Especially when used with various electronic media Which is what we are concerned with!

E.g., to see objects need to find edges ...

But, in effect “oversee” edges, e.g., Mach band And other things …

Simultaneous Brightness Contrast Gray patch on dark background looks lighter than

same patch on light background

Saw “Overdection” in GC Flat shading “looks worse than is…”

Mach banding at polygon edge for flat shading

Hermann Grid Illusion Black spots appear at intersections of bright lines

Couple of other things going on here …

So, … What perceived is NOT what is there!

Here, perceived edges, discontinuities, … … and flashing dots (for heaven’s sake)!

That way for evolutionary reasons System to detect edges …

For forming boundaries among things, to perceive objects … and in general work well

We’ve just been pushing systems boundaries Finding places where fail

Important to know where, and how, fails for designing visualizations

At core of explanation is that “neurons detect differences” … as Ware says Will examine how neurons work ~Feature extraction

Overview Neurons detect differences …

… and inhibit, as well as excite And are connected to many others, …., as we’ve discussed

Neurons, receptive fields, and brightness illusions Hermann grid, Mach bands, simultaneous brightness contrast

Contrast effects and artifacts in cg Lots of illustrations to complement theory

Edge enhancement

Luminance, brightness, and lightness Physical energy, and perceived reflectance/color Perception of surface lightness

Neurons Detect Differences Last time, saw that receptors act as transducers

Changing energy or chemicals to nerve signals

In fact, receptors transmit signals about relative (vs. absolute) amount of energy, e.g., light How light differs from one receptor to another How light has changed in past instant Ware:

“Neurons in the early stages of the visual system do not behave like light meters; they behave like change meters.”

Implication is that visualization not good for measuring absolute numerical values, but rather for displaying patterns of differences or changes over time

Again, nature of visual system leads to “errors” Especially in computer graphics

Visualization and Neurology Main point of today is that as visualization designers we

should:1. At least be “sensitive” to the occurrence of these errors2. As possible, be able to specify the conditions under which they occur

Below – gravitational field Neurologically detecting difference leads to Mach banding and contrast

errors

Neurons, Receptive Fields, and Brightness Illusions

In fact, considerable processing of information in eye itself Several layers of cells culminate

in retinal ganglion cells Recall, n retinal cells into

ganglion cells differs, as f (distance) fovea

Reception of retinal cells is by fields of neurons

Ganglion cells send information through optic nerve to lateral geniculate nucleus

Then, on to primary visual processing areas at back of brain, visual cortex

Receptive Fields Receptive field of a cell:

Visual area over which cell responds to light

Patterns of light falling on retina influence way neuron responds Even though may be many synapses

removed from receptors

Retinal ganglion cells organized with circular receptive fields that are either (1) on-center or (2) off-center Cells are firing constantly 1. For on-center

(from baseline firing rate): When stimulated in center of its

receptive field, it emits pulses at greater rate

When stimulated outside center of field, emits pulses at lower rate Inhibitory effect of edge

2. For off-center, the opposite

A. Receptive field structure of on-center cellB. Response in activity of array of on-center cells to being stimulated by a bright edge - Output of system: Enhanced response on bright side of edge - Cell fires more on bright side because there is less light in inhibitory region, hence less inhibited Depressed response on dark side of edge Intermediate to uniform areas on either side of edgeC. Smoothed plot of activity level

Receptive Fields – Another Graphical View

Again, 1. For on-center (from baseline firing rate) When stimulated in center of its receptive field, it emits pulses at greater

rate When stimulated outside center of field, emits pulses at lower rate

Inhibitory effect of edge And, can be on-center-off-surround or off-center-on-surround

Demo DoG in Photoshop

Center-surround Receptive Fields

Receptive fields distributed across retina (and overlap)

Work simultaneously to “enhance” and “suppress” rate of firing of collection of receptors in the field

Center-surround Receptive Fields Act as edge

detectors more than level detectors A: mid-low B: Lowest C: Highest D: mid-high

Hermann Grid Illusion

Black spots appear at intersections of bright lines There is more inhibition at points between two squares Hence, they seem brighter than at the points at the intersection

Hermann Grid Illusion with Receptive Fields

Black spots appear at intersections of bright lines There is more inhibition at points between two squares Hence, they seem brighter than at the points at the intersection

Simultaneous Brightness Contrast

Gray patch on a dark background looks lighter than the same patch on a light background

Simultaneous Brightness Contrast

Background removed! (honest, no change in foreground)

Simultaneous Brightness Contrast

Same phenomenon, again

Simultaneous Brightness Contrast

Gray patch on a dark background looks lighter than the same patch on a light background Predicted by DOG model of concentric opponent receptive fields

Mach Bands

At point where uniform area meets a luminance ramp, bright band is perceived Said another way, appear where abrupt change in first derivative of

brightness profile Simulated by DOG model Particularly a problem for uniformly shaded polygons in computer graphics

Hence, various methods of smoothing are applied

Ernst Mach

Mach Bands and Receptor Fields, 1

Point where uniform area meets luminance ramp, bright band is perceived Another way, appear where abrupt change in 1st derivative of

brightness profile Simulated by DOG model Particularly a problem for uniformly shaded polygons in computer

graphics Hence, various methods of smoothing are applied

The Chevreul Illusion

With sequence of gray bands, bands appear darker at one edge than another Simulated by application of DOG model Again, “over-detection” of differences

The Chevreul Illusion

Again

The Chevreul Illusion

The Chevreul Illusion Pixel arrays used

in rendering

The Chevreul Illusion At different iterations

Simultaneous Contrast and Error

Contrast effects are clear Overestimate differences as edges Even see things that aren’t there!

Lead to errors of judgment in extracting information from visual displays Gray scales, or any continuous tone, in particular lead to such errors E.g., gravitational map, error in extracting information of 20% of entire scale

Simultaneous Contrast and Error Contrast effects are clear

Overestimate differences as edges Even see things that aren’t there!

Lead to errors of judgment in extracting information from visual displays Gray scales, or any continuous tone, in particular lead to

such errors E.g., gravitational map, error in extracting information of

20% of entire scale

Contrast Effects and Artifacts in CG

As noted, for computer graphics Consequence of Mach bands,

etc. for shading algorithms At best loss of “realism”, at worst

perception of patterns at edges

Shading of facets (polygons) Uniform

1 value for a polygon Gouraud

Value for edges Average of surface normals at

boundaries where facets meet Interpolated between boundaries Still discontinuity at at facet

boundaries (edges) Phong

Surface normal interpolated between edges

No Mach bandingActual light Perceived/DOG

Another dangerous illusion!

Edge Enhancement: Cornsweet Effect

Lateral inhibition Can be considered 1st stage of an

edge detection process Signals positions and contrasts of

edges in environment Result is that “pseudo-edges” are

formed

Cornsweet effect 2 areas that physically have same

brightness can be made to look different by having an edge that shades off gradually to the 2 sides

Brain does perceptual interpolation, so that entire central region appear lighter than surrounding regions

Cornsweet in action! This is a more

extreme example of the Cornsweet effect. The top and bottom greys are the same shade of grey. I didn't believe that myself when I first saw this image. To prove the point, I extended the grey areas as shown below.

Cornsweet in action! Hold your

hand over the image on your computer screen so that you can only see the grey bands on the left on their own.

Edge Enhancement: Art and Visualization

Also used by artists Limited dynamic range of paint Important to make objects distinct Seurat Signat notes:

Observance of the laws of contrast, methodical separation of the elements (light, shadow, local color, reactions)

Visualization, generally Adjust background Make object stand out

Edge Enhancement: Seurat

Bathing at Asnieres

Edge Enhancement: Seurat

La Grande Jatte

Luminance, Brightness, Lightness Ecologically, need to be able to manipulate objects in

environment Information about quantity of light, of relatively little use

Rather, what need to know about its use

Human visual system evolved to extract surface properties Loose information about quantity and quality of light E.g., experience colored objects, not color light

Color constancy Similarly, overall reflectance of a surface

Lightness constancy

Luminance, Brightness, Lightness Consider physical stimulus and perception

Luminance Amount of light (energy) coming from region of space,

Measured as units energy / unit area E.g., foot-candles / square ft, candelas / square m Physical

Brightness Perceived amount of light coming from a source Here, will refer to things perceived as self-luminous

Lightness Perceived reflectance of a surface E.g., white surface is light, black surface is dark

– Physical• Luminance

– Number of photons coming from a region of space

– Perceptual:• Brightness

– Amount of light coming from a glowing source

• Lightness– Reflectance of a

surface, paint shade

Luminance Amount of light (energy) hitting the eye

To take into account human observer: Weighted by the sensitivity of the photoreceptors to each wavelength

Spectral sensitivity function:

E.g., humans about 100 times less sensitive to light at 450nm than at 510nm Note, use of blue for detail, e.g., text, not seem good

Compounded by chromatic aberration in which blue focuses at different point

Later, will examine difference cone sensitivities

700

400

EVL

Finer Detail Requires More Luminance Difference

Text: at least 3:1 10:1 preferred

Generalizes to data Detection of detail

requires more contrast

More detail -> More Contrast

Brightness Perceived amount of light coming from a glowing (self-

luminous) object E.g., instruments

Perceived brightness very non-linear function of the amount of light Shine a light of some intensity on a surface, and ask an observer,

“How bright?” Intensity = How bright is the point?” 1 1 4 2 16 4

- Steven’s power law

Intensity ->

Perceived ^Brightness |

Brightness – Power Law Stevens power law

Perceived sensation, S, is proportional to stimulus intensity, I, raised to a power, n

S = I n Here, Brightness = Luminancen

With n = 0.333 for patches of light, 0.5 for points Applies only to lights in relative isolation in dark, so application more

complicated

Applies to many other perceptual channels Loudness (dB), smell, taste, heaviness, force, friction, touch,

etc.

Enables high sensitivity at low levels without saturation at high levels

Intensity ->

Perceived ^Brightness |

Monitor Gamma Monitors in fact emit light in amounts that are not linearly related to

the voltage driving them

Historically, effort of early television engineers to most efficiently use available bandwidth

Exploits non-linearity of human perception Attempt to make linear change in voltage map for more closely to

linear perceptual difference Luminance = Voltage g

g is monitor gamma L ranges from 1.4 through 3 L=3 cancels n=0.33 Stevens’ function:

Brightness ~ (Voltage3)0.33 ~ Voltage

Precise control of luminance requires careful monitor measurement and calibration Can adjust on many monitors, as well as other corrections

ApplicabilityMonitor calibration http://www.youtube.com/watch?v=uEZxl_IM7FQ

Adaptation: Overall Light Level Amazing and high survival value Factor of 10,000 difference: sunlight to

moonlight Still can identify different-brightness

materials Absolute amount of light from surface

irrelevant Adaptation to change in overall light

level Overall level of illumination “factored

out” Allows relative changes in an environment

to be perceived Factor of 2 hardly noticeable Iris opens and closes (small effect) Receptors photobleach at high light

levels (large effect) Can take time to regenerate when

entering dark areas Eventually switch to rods

50 lux interior to 50,000 lux bright sunlight

Contrast and Constancy Various constancies One is lightness

constancy Easy to tell which piece

of paper is gray and which white

White paper is lighter relative to its background

Desk color is constant Contrast of object with

background provides cue for accurate perception

Perception of Surface Lightness

Perception of surface lightness, and lightness constancy depends on: Adaptation and contrast, as noted

Direction of illumination and surface orientation E.g., white surface turned away from light

source reflects less light than if turned toward light

Lightest object in scene serves as “reference white to determine gray values of other objects Cf., lightness scaling formulas

Ratio of specular to nonspecular reflection E.g., everything black vs. white, specular cues

Next class Visualization Context: Colour Readings:

Ware, Chapters 3 Michel Foucault, This Is Not A Pipe, Chapter Two:

The Unraveled Calligram (1983). Today in lab:

Fundamental Techniques in Photoshop CS4