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- Attendance (please sign in)
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- Homework 6
- Lecture
- Midterm Review
- If we finish early we leave early
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- Please complete all reading
- Review for the exam
- Schedule office hours to clear-up any questions
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- Next week: Monday November 1st
- We will have the exam followed by lecture
- 1 hour to complete exam
- Break
- Resume at 8 for brief lecture
- Exam will be closed book
- No calculator or computer
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- What did you think of the HW?
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- Most work
- Some:
- Only go RGB -> HSL
- Don’t display the resulting color
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- Surprising number of people requested C#
- OK, but really need to document code
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- Very nice to quickly build UI
- However:
- I don’t intend to hunt for your code between a dozen files full of auto
generated code and content
- Use a framework if you wish but
- Condense and comment the results
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- People think of color in terms of
- Hue, Saturation/Tint, and Luminance/Brightness
- People rarely consider the relative amounts of Red, Green, and Blue in a
color
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- Variable amounts of any three colors can define the same color
- Providing the color defined is within all gamuts
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- Easiest to program given an internal representation of color
- Some feeling of data representation
- Degrees, percentages, etc. as bits
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- Wanted you to deal with:
- Mapping colors between color spaces
- Some colors are not representable
- Specifically Mapping
- Computer’s RGB space into NTSC video
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- Need Phosphor Chromaticity:
- NTSC ‘theoretical’
- Common NTSC TV
- Common RGB Monitors
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- Brightest TV sells the best
- Brighter blue phosphor
- Whopping 9300K white
- (Compare to 6500K sunlight)
- At night ever walk past a window with a TV flickering?
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- Any color may be matched by mixing at most 3 lights
- Luminance of a color = sum of the luminance of its components
- Humans cannot distinguish components of a color match
- Metameric match is a pair indistinguishable colors with different
spectra
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- A color match works over a range of luminances
- If 2 colors match their mixtures will match
- If 2 colors match and matching colors are subtracted from each the
results match
- If C1 matches C2, and C2 matches C3,
then C1 matches C3
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- Commission Internationale de L’Eclairage
- CIE tristimulus color coords XYZ
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- Theory:
- Given CIE values of the 3 CRT monitor phosphors
- And the CIE values for the RGB the monitor uses
- Original colors can be matched
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- xyY: x,y chromaticity, Y luminance
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- Problem:
- Monitor specifications are rarely made available by the manufacturer
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- xyY=(0.285, 0.293, 1)
- XYZ=(0.973, 1, 1.440)
- Corresponds to 9300K white point!
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- Why isn’t CIE used in most file formats?
- Positives:
- Can encode all visible colors
- Colors are universal
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- Issues:
- Represents many colors not representable by hardware
- Larger color space requires more precision
- (bits to encode at same resolution)
- Most hardware is non-linear and poorly characterized
- PNG and proprietary formats encode RGB
- But provide additional information about the capture device
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- Matching device specific RGB colors
- (on, say, 2 video monitors)
- Accomplished by 1st converting into a common device
independent space
- (CIE space is convenient)
- Then converting back into the 2nd device’s RGB space using an
inverse matrix
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- This is a straightforward matrix operation
- See Miano p197 for details
- NOTE:
- Normalize all values to 0-1 ranges
- Resultings coordinates <0 or >1 are not representable by the
device
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- Two choices for dealing with illegal colors
- ‘Limit’ components of illegal colors
- Changes the color
- Changes the mix of colors
- Or compress the color space of the image so all colors ‘fit’
- This changes the range of colors
- Preserves the color ‘dynamics’
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- MacAdam ellipses describe regions of CIE space that humans perceive as
indistinguishable
- (Just noticeable difference)
- Initially used human subjects on discrimination tests
- Unsurprisingly human perception of CIE space isn’t uniform
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- Red ellipses -> measured
- Blue ellipses -> modeled
- Based on non-linearities in the cones
- From: Photoreceptor non-linearities can account for the MacAdam ellipses
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- Attempts to make color distance = difference
- CIE Uniform chromaticity Scale (UCS)
- uvY
- UVW
- Lab
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- Most systems have non-linear response
- Consider:
- Film
- RGB monitors
- Photoreceptors in our Eyes
- Gamma represents the response curve
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- Gamma of 1: linear
- Gamma < 1: low contrast
- Provides more latitude in dark region
- Gamma > 1: high contrast
- CRT monitors: typically 2.0-3.0
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- Most systems don’t provide HW gamma correction
- Correspondingly use SW to change the RGB values for pixels in an image
- Gamma < 1
- Assigns more values to darker colors
- (think palate)
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- High end systems
- Like the SGI systems I build
- Use an 8 bit gamma palate per RGB channel
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- Gamma more than a function of hardware
- Gamma also changes based on lighting
- Gamma can be approximated
- Using specially prepared image
- Trading spatial illumination
- ½ pixels black, ½ pixel max
- Should equal a region of ½ intensity
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- Important to calibrate gamma when creating images
- Unfortunately PC, Web Content, etc.
- Authored expecting non-linear gamma
- Typical PC and Mac gammas differ
- Content created for 1
- Looks bad the another
- Looks bad on properly calibrated display
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- Properly characterized
- Display devices
- Video output
- Image formats in device independent color space
- Display programs that map device independent to device dependent color
- Hardware acceleration (so there isn’t a ‘penalty’
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- Don’t expect memorization
- Other than important constants and concepts
- Pays to recall if relationships are
- Linear
- Geometric
- Asymptotic
- Inverse
- Etc.
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- I don’t like multiple choice/fill in the blank
- Prefer questions that require thought and demonstrate understanding
- Expect to:
- Work mathematical problems
- Graph
- Write simple programs
- Justify a conclusion
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- Review all lecture notes
- Will be emphasizing what was covered in class
- Complete all reading
- Will expect understanding of this material if not memorization
- Be able to reproduce any homework
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- Data representation
- Human Perception
- Just noticeable difference
- Sensors
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- Information theory
- Entropy Coding
- Dictionary coding
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- Color
- Color Perception
- Tristimulus color
- Color matching
- JND
- Color Representation
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