1. UMCP ENEE631 Slides (created by M.Wu © 2004)
Based on ENEE631 Spring’04 Section 2
Visual Perception
UMCP ENEE631 Slides (created by M.Wu © 2004)
Spring ’04 Instructor: Min Wu ECE Department, Univ. of Maryland, College Park
(select ENEE631 S’04)

2. UMCP ENEE631 Slides (created by M.Wu © 2004)
Overview
Last Class
Introduction to DIP/DVP
applications and examples
Image as a function
Concepts of sampling and quantization
Today
Human visual perception: monochrome vision and color vision
UMCP ENEE631 Slides (created by M.Wu © 2004)
ENEE631 Digital Image Processing (Spring'04)

3. Information Processing by Human Observer
image
eye
perceived image
understanding of content
UMCP ENEE631 Slides (created by M.Wu © 2001)
Visual perception
Concerns how an image is perceived by a human observer
preliminary processing by eye  this lecture
further processing by brains
Important for developing image fidelity measures
needed for design and evaluate DIP/DVP algorithms & systems
ENEE631 Digital Image Processing (Spring'04)

4. UMCP ENEE631 Slides (created by M.Wu © 2004)
The Eye
UMCP ENEE631 Slides (created by M.Wu © 2004)
Figure is from slides at Gonzalez/ Woods DIP book website (Chapter 2)
Cross section illustration
ENEE631 Digital Image Processing (Spring'04)

5. Two Types of Photoreceptors at Retina
4/17/2017
Two Types of Photoreceptors at Retina
Rods
Long and thin
Large quantity (~ 100 million)
Provide scotopic vision (i.e., dim light vision or at low illumination)
Only extract luminance information and provide a general overall picture
Cones
Short and thick, densely packed in fovea (center of retina)
Much fewer (~ 6.5 million) and less sensitive to light than rods
Provide photopic vision (i.e., bright light vision or at high illumination)
Help resolve fine details as each cone is connected to its own nerve end
Responsible for color vision
Mesopic vision
provided at intermediate illumination by both rod and cones
UMCP ENEE631 Slides (created by M.Wu © 2001/2004)
Photoreceptors – also called light receptor (receptor is sense organ that is a cell or groups of cells that receives stimuli)
Color objects often appear as colorless in dim light because only the rods are stimulated.
our interest (well-lighted display)
ENEE631 Digital Image Processing (Spring'04)

6. UMCP ENEE631 Slides (created by M.Wu © 2004)
Light
Figure is from slides at Gonzalez/ Woods DIP book website (Chapter 2)
UMCP ENEE631 Slides (created by M.Wu © 2004)
Light is an electromagnetic wave
with wavelength of 350nm to 780nm stimulating human visual response
Expressed as spectral energy distribution I()
The range of light intensity levels that human visual system can adapt is huge: ~ on 10 orders of magnitude (1010) but not simultaneously
Brightness adaptation: small intensity range to discriminate simultaneously
ENEE631 Digital Image Processing (Spring'04)

7. Luminance vs. Brightness
Same lum. Different brightness
Different lum. Similar brightness
Luminance (or intensity)
Independent of the luminance of surroundings
I(x,y,) -- spatial light distribution
V() -- relative luminous efficiency func. of visual system ~ bell shape
(different for scotopic vs. photopic vision; highest for green wavelength, second for red, and least for blue )
Brightness
Perceived luminance
Depends on surrounding luminance
UMCP ENEE631 Slides (created by M.Wu © 2001/2004)
ENEE631 Digital Image Processing (Spring'04)

8. Luminance vs. Brightness (cont’d)
Example: visible digital watermark
How to make the watermark appears the same graylevel all over the image?
UMCP ENEE631 Slides (created by M.Wu © 2001)
from IBM Watson web page “Vatican Digital Library”
ENEE631 Digital Image Processing (Spring'04)

9. Look into Simultaneous Contrast Phenomenon
4/17/2017
Look into Simultaneous Contrast Phenomenon
Human perception more sensitive to luminance contrast than absolute luminance
Weber’s Law: | Ls – L0 | / L0 = const
Luminance of an object (L0) is set to be just noticeable from luminance of surround (Ls)
For just-noticeable luminance difference L:
L / L  d( log L )  0.02 (const)
equal increments in log luminance are perceived as equally different
Empirical luminance-to-contrast models
Assume L  [1, 100], and c  [0, 100]
c = 50 log10 L (logarithmic law, widely used)
c = 21.9 L1/3 (cubic root law)
UMCP ENEE631 Slides (created by M.Wu © 2001/2004)
Luminance-to-contrast model example: see Jain’s Fig.3.4 (pp52)
ENEE631 Digital Image Processing (Spring'04)

10. UMCP ENEE631 Slides (created by M.Wu © 2004)
Mach Bands
Figure is from slides at Gonzalez/ Woods DIP book website (Chapter 2)
UMCP ENEE631 Slides (created by M.Wu © 2004)
Visual system tends to undershoot or overshoot around the boundary of regions of different intensities
 Demonstrates the perceived brightness is not a simple function of light intensity
ENEE631 Digital Image Processing (Spring'04)

11. Visual Angle and Spatial Frequency
Visual angle matters more than absolute distance
Smaller but closer object vs. larger but farther object
Eyes can distinguish about lines per degree in bright illumination
25 lines per degree translate to 500 lines if distance=4*screenheight
Spatial Frequency
Measures the extent of spatial transition
in unit of “cycles per visual degree”
dot
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
ENEE631 Digital Image Processing (Spring'04)

12. Visibility Threshold at Various Spatial Frequency
Preliminaries on 2-D linear (spatial) invariant system
[ Extending from 1-D LTI systems ]
Can be characterized by “Point Spread Function (PSF)” (i.e. impulse response) and the 2-D transfer function
The magnitude of the (normalized) transfer function is called the “Modulation Transfer Function (MTF)”
We’ll study 2-D systems and transforms in detail in 2 lectures
Visibility threshold at different spatial frequency
Eyes are most sensitive to mid frequencies, and least sensitive to high frequencies
Most sensitive to horizontal and vertical ones than other orientations
UMCP ENEE631 Slides (created by M.Wu © 2004)
ENEE631 Digital Image Processing (Spring'04)

13. Monochrome Vision Models
See Jain’s Fig.3.7 (pp55) Fig.3.9 (pp57)
Monochrome Vision Models
MTF of Human Visual System (HVS)
Modulation Transfer Func. (MTF)
from experiment with sinusoidal grating of varying contrast
similar to a band-pass filter
most sensitive to mid frequencies
least sensitive to high frequencies
also depends on the orientation of grating
most sensitive to horizontal and vertical ones
Overall monochrome vision models
How light is transformed by eye to brightness information
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

14. Image Fidelity Criteria
Subjective measures
Examination by human viewers
Goodness scale: excellent, good, fair, poor, unsatisfactory
Impairment scale: unnoticeable, just noticeable, …
Comparative measures
with another image or among a group of images
Objective (Quantitative) measures
Mean square error and variations
Pro:
Simple, less dependent on human subjects, & easy to handle mathematically
Con:
Not always reflect human perception
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

15. Mean-square Criterion
Average (or sum) of squared difference of pixel luminance between two images
Signal-to-noise ratio (SNR)
SNR = 10 log10 ( s2 / e2 ) in unit of decibel (dB)
s2 – image variance, e2 – variance of noise or error
PSNR = 10 log10 ( A2 / e2 ) with A being peak-to-peak value
PSNR is about dB higher than SNR
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

16. UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
Color of Light
Perceived color depends on spectral content (wavelength composition)
e.g., 700nm ~ red.
“spectral color”
A light with very narrow bandwidth
A light with equal energy in all visible bands appears white
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
“Spectrum” from
ENEE631 Digital Image Processing (Spring'04)

17. Perceptual Attributes of Color
Value of Brightness (perceived luminance)
Chrominance
Hue
specify color tone (redness, greenness, etc.)
depend on peak wavelength
Saturation
describe how pure the color is
depend on the spread (bandwidth) of light spectrum
reflect how much white light is added
RGB  HSV Conversion ~ nonlinear
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
HSV circular cone is from online documentation of Matlab image processing toolbox
ENEE631 Digital Image Processing (Spring'04)

18. Absorption of Light by R/G/B Cones
Figure is from slides at Gonzalez/ Woods DIP book website (Chapter 6)
UMCP ENEE631 Slides (created by M.Wu © 2004)
ENEE631 Digital Image Processing (Spring'04)

19. Representation by Three Primary Colors
4/17/2017
Representation by Three Primary Colors
Any color can be reproduced by mixing an appropriate set of three primary colors (Thomas Young, 1802)
Three types of cones in human retina
Absorption response Si() has peaks around 450nm (blue), 550nm (green), 620nm (yellow-green)
Color sensation depends on the spectral response {1(C), 2(C), 3(C) } rather than the complete light spectrum C()
UMCP ENEE631 Slides (created by M.Wu © 2001/2004)
See Gonzalez figure (next slides) or Jain’s Fig.3.11 (pp61)
 S1() C() d 
 S2() C() d 
 S3() C() d 
C()
color light
1(C)
2(C)
3(C)
Identically perceived colors if i (C1) = i (C2)
ENEE631 Digital Image Processing (Spring'04)

20. Example: Seeing Yellow Without Yellow
4/17/2017
Example: Seeing Yellow Without Yellow
520nm
630nm
570nm =
UMCP ENEE408G/631 Slides (created by M.Wu & R.Liu © 2002/2004)
If the mix of green and red light trigger the same sensation of the R/G/B cones as the yellow light does, we will see the mix as yellow.
mix green and red light to obtain perception of yellow, without shining a single yellow photon
“Seeing Yellow” figure is from B.Liu ELE330 S’01 lecture Princeton; R/G/B cone response is from slides at Gonzalez/ Woods DIP book website
ENEE631 Digital Image Processing (Spring'04)

21. Color Matching and Reproduction
Mixture of three primaries: C = Sum(k Pk () )
To match a given color C1
adjust k such that i (C1) = i (C), i = 1,2,3.
Tristimulus values Tk (C)
Tk (C) = k / wk
wk – the amount of kth primary to match the reference white
Chromaticity tk = Tk / (T1+T2+T3)
t1+t2+t3 = 1
visualize (t1, t2 ) to obtain chromaticity diagram
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

22. UMCP ENEE631 Slides (created by M.Wu © 2001)
Chromaticity Diagram
Chromaticity Diagram
Color gamut
Reference white
Line of purples
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

23. CIE Color Coordinates (cont’d)
CIE XYZ system
hypothetical primary sources to yield all-positive spectral tristimulus values
Y ~ luminance
Color gamut of 3 primaries
Colors on line C1 and C2 can be produced by linear mixture of the two
Colors inside the triangle gamut can be reproduced by three primaries
UMCP ENEE631/408G Slides (created by M.Wu & R.Liu © 2001/2002)
From
ENEE631 Digital Image Processing (Spring'04)

24. RGB Primaries and Color Representation
Use red, green, blue light to represent a large number of visible colors
The contribution from each primary is normalized to [0, 1]
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
Color-cube figures: left figure is from B.Liu ELE330 S’01 lecture Princeton, right figure is from slides at Gonzalez/ Woods DIP book website
ENEE631 Digital Image Processing (Spring'04)

25. Color Coordinate for Printing
4/17/2017
Color Coordinate for Printing
Primary colors for pigment
Defined as one that subtracts/absorbs a primary color of light & reflects the other two
CMY – Cyan, Magenta, Yellow
Complementary to RGB
Proper mix of them produces black
UMCP ENEE408G/631 Slides (created by M.Wu & R.Liu © 2002/2004)
Primary colors (CMY) vs secondary colors (RGB)
Yellow light is a mix of R+G => yellow pigment absorb blue and emit R+G, …
Figure is from slides at Gonzalez/ Woods DIP book website (Chapter 6)
ENEE631 Digital Image Processing (Spring'04)

26. UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
Examples
RGB
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
HSV
YUV
ENEE631 Digital Image Processing (Spring'04)

27. Color Coordinates Used in TV Transmission
Facilitate sending color video via 6MHz mono TV channel
YIQ for NTSC (National Television Systems Committee) transmission system
Use receiver primary system (RN, GN, BN) as TV receivers standard
Transmission system use (Y, I, Q) color coordinate
Y ~ luminance, I & Q ~ chrominance
I & Q are transmitted in through orthogonal carriers at the same freq.
YUV (YCbCr) for PAL and digital video
Y ~ luminance, Cb and Cr ~ chrominance
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
ENEE631 Digital Image Processing (Spring'04)

28. UMCP ENEE631 Slides (created by M.Wu © 2001)
Color Coordinates
RGB of CIE
XYZ of CIE
RGB of NTSC
YIQ of NTSC
YUV (YCbCr)
CMY
UMCP ENEE631 Slides (created by M.Wu © 2001)
ENEE631 Digital Image Processing (Spring'04)

29. UMCP ENEE631 Slides (created by M.Wu © 2004)
Summary
Monochrome human vision
visual properties: luminance vs. brightness, etc.
image fidelity criteria
Color
Color representations and three primary colors
Color coordinates
Next time:
Pixel-based operation
Reading Assignment:
Chapter ; Of Gonzalez’s book
(or) Chapter 3 of Jain’s book;
Sec.1.1 of Wang’s book
UMCP ENEE631 Slides (created by M.Wu © 2004)
ENEE631 Digital Image Processing (Spring'04)