Lightness, Brightness, Contrast, and Constancy Ware Chapter 3 University of Texas – Pan American CSCI 6361, Spring 2014

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 works 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 –relative - How light differs from one receptor to another (spatial) –relative - How light has changed in past instant (temporal) –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 tonight 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 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, opposite 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 But, let’s keep it simple

Center-surround Receptive Fields Receptive fields distributed across retina (and overlap) –Overlap not shown at rt. 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 – in ex at rt: A: mid-low B: Lowest C: Highest D: mid-high

Receptive Fields: DOG Model (opt.) Basic idea is that patterns are detected – will just mention … –And this model works well Firing rate of cells: –One Gaussian distribution represents center, other represent surround And firing rate is difference Difference of Gaussians (DOG) model Where, –x = distance from center of field –w 1 = width of center –w 2 = width of surround –  1,  2 amplitude parameters = amount of excitation or inhibition Can calculate effect of DOG-type receptor on patterns –Think of pattern passing over receptive field of cell, or output of whole array of DOG cells arranged in a line across the pattern –DOG receptive field can be used to explain variety of brightness contrast effects

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 –Or, are they really not the same? … bets?

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 Saw the phenomenon, what’s going on?

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 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

Simultaneous Contrast and Error Contrast effects are clear –Overestimate differences as edges –Even see things that aren’t there! Leads 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 banding Actual light Perceived/DOG

Edge Enhancement: Cornsweet Effect Lateral inhibition –Can be considered 1 st 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: 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 (physical) –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 (perceptual) –Perceived amount of light coming from a source –Here, will refer to things perceived as self- luminous Lightness (perceptual) –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

Finer Detail Requires More Luminance Difference Recall, from last time spatial contrast sensitivity function Text: at least 3:1 –10:1 preferred Generalizes to data –Detection of detail requires more contrast More detail -> More Contrast

Brightness Perceived brightness and physical intensity 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?” (physical) (perceptual) 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 = Luminance n –With n = 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  –  is monitor gamma –L ranges from 1.4 through 3 –L=3 cancels n=0.33 Stevens’ function: Brightness ~ (Voltage 3 ) 0.33 ~ Voltage Precise control of luminance requires careful monitor measurement and calibration –Can adjust on many monitors, as well as other corrections

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

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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 (factored in by system 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