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Digital Cameras Engineering Math Physics (EMP) Jennifer Rexford http://www.cs.princeton.edu/~jrex Image Transmission Over Wireless Networks Image capture and compression Inner-workings of a digital camera Manipulating & transforming a matrix of pixels

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digital cameras

Digital Cameras

Engineering Math Physics (EMP)

Jennifer Rexford

http://www.cs.princeton.edu/~jrex

image transmission over wireless networks
Image Transmission Over Wireless Networks
  • Image capture and compression
    • Inner-workings of a digital camera
    • Manipulating & transforming a matrix of pixels
    • Implementing a variant of JPEG compression
  • Wireless networks
    • Wireless technology
    • Acoustic waves and electrical signals
    • Radios
  • Video over wireless networks
    • Video compression and quality
    • Transmitting video over wireless
    • Controlling a car over a radio link
traditional photography
Traditional Photography
  • A chemical process, little changed from 1826
  • Taken in France on a pewter plate
  • … with 8-hour exposure

The world's first photograph

image formation
Image Formation

Digital Camera

Film

Eye

image formation in a pinhole camera
Image Formation in a Pinhole Camera
  • Light enters a darkened chamber through pinhole opening and forms an image on the further surface
aperture
Aperture
  • Hole or opening where light enters
    • Or, the diameter of that hole or opening
  • Pupil of the human eye
    • Bright light: 1.5 mm diameter
    • Average light: 3-4 mm diameter
    • Dim light: 8 mm diameter
  • Camera
    • Wider aperture admits more light
    • Though leads to blurriness in theobjects away from point of focus
shutter speed
Shutter Speed
  • Time for light to enter camera
    • Longer times lead to more light
    • … though blurs moving subjects
  • Exposure
    • Total light entering the camera
    • Depends on aperture and shutter speed
digital photography
Digital Photography
  • Digital photography is an electronic process
  • Only widely available in the last ten years
  • Digital cameras now surpass film cameras in sales
image formation in a digital camera
Image Formation in a Digital Camera

+10V

Photon

+ + + + + +

+

A sensor converts one

kind of energy to another

  • Array of sensors
    • Light-sensitive diodes convert photons to electrons
    • Buckets that collect charge in proportion to light
    • Each bucket corresponds to a picture element (pixel)
ccd charge coupled device
CCD: Charge Coupled Device

CCD sensor

  • Common sensor array used in digital cameras
    • Each capacitor accumulates charge in response to light
  • Responds to about 70% of the incident light
    • In contrast, photographic film captures only about 2%
  • Also widely used in astronomy telescopes
sensor array image sampling
Sensor Array: Image Sampling

Pixel (Picture Element): single point in a graphic image

sensor array reading out the pixels
Sensor Array: Reading Out the Pixels
  • Transfer the charge from one row to the next
  • Transfer charge in the serial register one cell at a time
  • Perform digital to analog conversion one cell at a time
  • Store digital representation

Digital-to-analog conversion

more pixels mean more detail
More Pixels Mean More Detail

1280 x 960

1600 x 1400

640 x 480

slide15

The 2272 x 1704hand

The 320 x 240hand

representing color
Representing Color
  • Light receptors in the human eye
    • Rods: sensitive in low light, mostly at periphery of eye
    • Cones: only at higher light levels, provide color vision
    • Different types of cones for red, green, and blue
  • RGB color model
    • A color is some combination of red, green, and blue
    • Intensity value for each color
      • 0 for no intensity
      • 1 for high intensity
    • Examples
      • Red: 1, 0, 0
      • Green: 0, 1, 0
      • Yellow: 1, 1, 0
representing image as a 3d matrix
Representing Image as a 3D Matrix
  • In the lab this week…
    • Matlab experiments with digital images
  • Matrix storing color intensities per pixel
    • Row: from top to bottom
    • Column: from left to right
    • Color: red, green, blue
  • Examples
    • M(3,2,1): third row, second column, red intensity
    • M(4,3,2): fourth row, third column, green intensity

1

2

3

1

2

limited granularity of color
Limited Granularity of Color
  • Three intensities, one per color
    • Any value between 0 and 1
  • Storing all possible values take a lot of bits
    • E.g., storing 0.368491029692069439604504560106
    • Can a person really differentiate from 0.36849?
  • Limiting the number of intensity settings
    • Eight bits for each color
    • From 00000000 to 11111111
    • With 28 = 256 values
  • Leading to 24 bits per pixel
      • Red: 255, 0, 0
      • Green: 0, 255, 0
      • Yellow: 255, 255, 0
number of bits per pixel
Number of Bits Per Pixel
  • Number of bits per pixel
    • More bits can represent a wider range of colors
    • 24 bits can capture 224 = 16,777,216 colors
    • Most humans can distinguish around 10 million colors

8 bits / pixel / color

4 bits / pixel / color

separate sensors per color
Separate Sensors Per Color
  • Expensive cameras
    • A prism to split the light into three colors
    • Three CCD arrays, one per RGB color
practical color sensing bayer grid
Practical Color Sensing: Bayer Grid
  • Place a small color filter over each sensor
  • Each cell captures intensity of a single color
  • More green pixels, since human eye is better at resolving green
practical color sensing interpolating
Practical Color Sensing: Interpolating
  • Challenge: inferring what we can’t see
    • Estimating pixels we do not know
  • Solution: estimate based on neighboring pixels
    • E.g., red for non-red cell averaged from red neighbors
    • E.g., blue for non-blue cell averaged from blue neighbors

Estimate “R” and “B” at the “G” cells from neighboring values

interpolation
Interpolation
  • Examples of interpolation
  • Accuracy of interpolation
    • Good in low-contrast areas (neighbors mostly the same)
    • Poor with sharp edges (e.g., text)

and

makes

and

makes

and

makes

are more pixels always better
Are More Pixels Always Better?
  • Generally more is better
    • Better resolution of the picture
    • Though at some point humans can’t tell the difference
  • But, other factors matter as well
    • Sensor size
    • Lens quality
    • Whether Bayer grid is used
  • Problem with too many pixels
    • Very small sensors catch fewer photons
    • Much higher signal-to-noise ratio
  • Plus, more pixels means more storage…
digital images require a lot of storage
Digital Images Require a Lot of Storage
  • Three dimensional object
    • Width (e.g., 640 pixels)
    • Height (e.g., 480 pixels)
    • Bits per pixel (e.g., 24-bit color)
  • Storage is the product
    • Pixel width * pixel height * bits/pixel
    • Divided by 8 to convert from bits to bytes
  • Example sizes
    • 640 x 480: 1 Megabyte
    • 800 x 600: 1.5 Megabytes
    • 1600 x 1200: 6 Megabytes
compression
Compression
  • Benefits of reducing the size
    • Consume less storage space and network bandwidth
    • Reduce the time to load, store, and transmit the image
  • Redundancy in the image
    • Neighboring pixels often the same, or at least similar
    • E.g., the blue sky
  • Human perception factors
    • Human eye is not sensitive to high frequencies
joint photographic experts group
Joint Photographic Experts Group
  • Starts with an array of pixels in RGB format
    • With one number per pixel for each of the three colors
    • And outputs a smaller file with some loss in quality
  • Exploits both redundancy and human perception
    • Transforms data to identify parts that humans notice less
    • More about transforming the data in Wednesday’s class

Uncompressed: 167 KB

Good quality: 46 KB

Poor quality: 9 KB

conclusion
Conclusion
  • Conversion of information
    • Light (photons) and a optical lens
    • Charge (electrons) and electronic devices
    • Bits (0s and 1s) and a digital computer
  • Combines many disciplines
    • Physics: lenses and light
    • Electrical engineering: charge coupled device
    • Computer science: manipulating digital representations
    • Mathematics: compression algorithms
    • Psychology/biology: human perception
  • Next class: compression algorithms