perception and measurement of light, color, and appearance
DESCRIPTION
Perception and Measurement of Light, Color, and Appearance. Problems. How do cameras measure light and color? Radiometry How do humans perceive light and color? Photometry How do monitors display light and color?. Perceived brightness. Amount of light. Intensity. - PowerPoint PPT PresentationTRANSCRIPT
Perception and Measurement Perception and Measurement ofof
Light, Color, and AppearanceLight, Color, and Appearance
ProblemsProblems
• How do cameras measure light and How do cameras measure light and color?color?– RadiometryRadiometry
• How do humans perceive light and How do humans perceive light and color?color?– PhotometryPhotometry
• How do monitors display light and How do monitors display light and color?color?
IntensityIntensity
• Perception of intensity is nonlinearPerception of intensity is nonlinear
Amount of lightAmount of light
PerceivedPerceivedbrightnessbrightness
Modeling Nonlinear Intensity Modeling Nonlinear Intensity ResponseResponse
• Brightness (Brightness (BB) usually modeled as a ) usually modeled as a logarithm or power law of intensity (logarithm or power law of intensity (II))
• Exact curve varies with ambient light,Exact curve varies with ambient light,adaptation of eyeadaptation of eye
3/1
log
IB
IkB
3/1
log
IB
IkB
II
BB
CRT ResponseCRT Response
• Power law for Intensity (Power law for Intensity (II) vs.) vs.applied voltage (applied voltage (VV))
• Other displays (e.g. LCDs) contain Other displays (e.g. LCDs) contain electronics to emulate this lawelectronics to emulate this law
5.2
VI
5.2
VI
Digression: Monitor KnobsDigression: Monitor Knobs
• ““Brightness” knob is offsetBrightness” knob is offset
• ““Contrast” knob is scaleContrast” knob is scale
• Yes, the names Yes, the names areare misleading… misleading…
)( brightnessVcontrastI )( brightnessVcontrastI
CamerasCameras
• Original cameras based on Vidicon obey Original cameras based on Vidicon obey power law for Voltage (V) vs. Intensity power law for Voltage (V) vs. Intensity (I):(I):
• Vidicon + CRT = almost linear!Vidicon + CRT = almost linear!
45.0
IV
45.0
IV
CCD CamerasCCD Cameras
• Camera gamma codified in NTSC Camera gamma codified in NTSC standardstandard
• CCDs have linear response to incident CCDs have linear response to incident lightlight
• Electronics to apply required power lawElectronics to apply required power law
• So, pictures from most cameras So, pictures from most cameras (including digital still cameras) will have (including digital still cameras) will have = 0.45= 0.45
Consequences for VisionConsequences for Vision
• Output of most cameras is not linearOutput of most cameras is not linear
• Know what it is! (Sometimes system Know what it is! (Sometimes system automagically applies “gamma correction”)automagically applies “gamma correction”)
• Necessary to correct raw pixel values for:Necessary to correct raw pixel values for:– Reflectance measurementsReflectance measurements
– Shape from shadingShape from shading
– Photometric stereoPhotometric stereo
– Recognition under variable lightingRecognition under variable lighting
Consequences for VisionConsequences for Vision
• What about e.g. edge detection?What about e.g. edge detection?– Often want “perceptually significant” edgesOften want “perceptually significant” edges
– Standard nonlinear signal close to (inverse Standard nonlinear signal close to (inverse of)of)human responsehuman response
– Using nonlinear signal often the “right Using nonlinear signal often the “right thing”thing”
Contrast SensitivityContrast Sensitivity
• Contrast sensitivity for humans about Contrast sensitivity for humans about 1%1%
• 8-bit image (barely) adequate if using 8-bit image (barely) adequate if using perceptual (nonlinear) mappingperceptual (nonlinear) mapping
• Frequency dependent: contrast Frequency dependent: contrast sensitivity lower for high and very low sensitivity lower for high and very low frequenciesfrequencies
Contrast SensitivityContrast Sensitivity
• Campbell-Robson contrast sensitivity Campbell-Robson contrast sensitivity chartchart
Bits per Pixel – Scanned PicturesBits per Pixel – Scanned Pictures
8 bits / pixel / color8 bits / pixel / color 6 bits / pixel / color6 bits / pixel / color
Marc Levoy / Hanna-BarberaMarc Levoy / Hanna-Barbera
Bits per Pixel – Scanned Pictures Bits per Pixel – Scanned Pictures (cont.)(cont.)
5 bits / pixel / color5 bits / pixel / color 4 bits / pixel / color4 bits / pixel / color
Marc Levoy / Hanna-BarberaMarc Levoy / Hanna-Barbera
Bits per Pixel – Line DrawingsBits per Pixel – Line Drawings
8 bits / pixel / color8 bits / pixel / color 4 bits / pixel / color4 bits / pixel / color
Marc Levoy / Hanna-BarberaMarc Levoy / Hanna-Barbera
Bits per Pixel – Line Drawings Bits per Pixel – Line Drawings (cont.)(cont.)
3 bits / pixel / color3 bits / pixel / color 2 bits / pixel / color2 bits / pixel / color
Marc Levoy / Hanna-BarberaMarc Levoy / Hanna-Barbera
ColorColor
• Two types of receptors: rods and Two types of receptors: rods and conescones
Rods and conesRods and cones Cones in foveaCones in fovea
Rods and ConesRods and Cones
• RodsRods– More sensitive in low light: “scotopic” visionMore sensitive in low light: “scotopic” vision
– More dense near peripheryMore dense near periphery
• ConesCones– Only function with higher light levels:Only function with higher light levels:
“photopic” vision“photopic” vision
– Densely packed at center of eye: foveaDensely packed at center of eye: fovea
– Different types of cones Different types of cones color vision color vision
ColorColor
• 3 types of cones: L, M, S3 types of cones: L, M, S
Tristimulus ColorTristimulus Color
• Any distribution of light can be summarized Any distribution of light can be summarized by its effect on 3 types of conesby its effect on 3 types of cones
• Therefore, human perception of color is aTherefore, human perception of color is a3-dimensional space3-dimensional space
• MetamerismMetamerism: different spectra, same : different spectra, same responseresponse
• Color blindness: fewer than 3 types of conesColor blindness: fewer than 3 types of cones– Most commonly L cone = M coneMost commonly L cone = M cone
ColorspacesColorspaces
• Different ways of parameterizing 3D Different ways of parameterizing 3D spacespace
• RGBRGB– Official standard: R = 645.16 nm,Official standard: R = 645.16 nm,
G = 526.32 nm, B = 444.44 nmG = 526.32 nm, B = 444.44 nm
– Most monitors are some approximation to Most monitors are some approximation to thisthis
XYZ ColorspaceXYZ Colorspace
• RGB can’t represent all pure RGB can’t represent all pure wavelengths with positive valueswavelengths with positive values– Saturated greens would require negative Saturated greens would require negative
redred
• XYZ colorspace is a linear transform of XYZ colorspace is a linear transform of RGB so that all pure wavelengths have RGB so that all pure wavelengths have positive valuespositive values
CIE Chromaticity DiagramCIE Chromaticity Diagram
Colorspaces for TelevisionColorspaces for Television
• Differences in brightness more Differences in brightness more important than differences in colorimportant than differences in color
• YCYCrrCCbb, YUV, YIQ colorspaces = linear , YUV, YIQ colorspaces = linear
transforms of RGBtransforms of RGB– Lightness: Y=0.299R+0.587G+0.114BLightness: Y=0.299R+0.587G+0.114B
– Other color components typically allocated Other color components typically allocated less bandwidth than Yless bandwidth than Y
Perceptually-Uniform ColorspacesPerceptually-Uniform Colorspaces
• Most colorspaces not Most colorspaces not perceptually uniformperceptually uniform
• MacAdam ellipses: MacAdam ellipses: color within each color within each ellipse appears ellipse appears constant (shown constant (shown here 10X size)here 10X size)
Perceptually-Uniform ColorspacesPerceptually-Uniform Colorspaces
• u’v’ spaceu’v’ space
• Not perfect, but better than XYZNot perfect, but better than XYZ
ZYX
Yv
ZYX
Xu
315
9'
315
4'
L*a*b* Color SpaceL*a*b* Color Space
• Another choice: L*a*b*Another choice: L*a*b*
3/13/1
3/13/1
3/1
200*
500*
16116*
nn
nn
n
Z
Z
Y
Yb
Y
Y
X
Xa
Y
YL
L*a*b* Color SpaceL*a*b* Color Space
• Often used for color comparison when Often used for color comparison when “perceptual” differences matter“perceptual” differences matter