Download - Lightness, Brightness and Contrast
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