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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

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lightness brightness and contrast

Lightness, Brightness and Contrast

Week 3 :CCT370 – Introduction to Computer Visualization

the big picture again
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
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
Simultaneous Brightness Contrast
  • Gray patch on dark background looks lighter than same patch on light background
saw overdection in gc
Saw “Overdection” in GC
  • Flat shading “looks worse than is…”
    • Mach banding at polygon edge for flat shading
slide7
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
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
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
Visualization and Neurology
  • Main point of today is that as visualization designers we should:

1. At least be “sensitive” to the occurrence of these errors

2. 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 geniculatenucleus

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

Neurons, Receptive Fields, and Brightness Illusions
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

Receptive Fields

A. Receptive field structure of on-center cell

B. 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 edge

C. 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

Receptive Fields – Another Graphical View
slide14
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

Center-surround Receptive Fields
hermann grid illusion1
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
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

Hermann Grid Illusion with Receptive Fields
simultaneous brightness contrast4
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

Simultaneous Brightness Contrast
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

Mach Bands

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

Mach Bands and Receptor Fields, 1
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
the chevreul illusion3
The Chevreul Illusion
  • Pixel arrays used in rendering
the chevreul illusion4
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
simultaneous contrast and error1
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 banding

Contrast Effects and Artifacts in CG

Actual light Perceived/DOG

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

Edge Enhancement: Cornsweet Effect
cornsweet in action
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 action1
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: Art and Visualization
luminance brightness lightness
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 lightness1
Luminance, Brightness, Lightness
  • 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
  • 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
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

Luminance
finer detail requires more luminance difference
Text: at least 3:1

10:1 preferred

Generalizes to data

Detection of detail requires more contrast

Finer Detail Requires More Luminance Difference

More detail -> More Contrast

brightness
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
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
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 availablebandwidth
    • 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
applicability
Applicability

Monitor 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

Adaptation: Overall Light Level

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

Contrast and Constancy
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

Perception of Surface Lightness
next class
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