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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

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
Image Formation

Digital Camera

Film

Eye

aperture and exposure
Aperture and Exposure
  • Aperture
    • Diameter of the hole allowing light to enter
    • E.g., the pupil of the eye
    • Higher aperture leads to more light entering
    • … though poorer focus across a wider depth of field
  • Shutter speed
    • Time for light to enter the camera
    • Longer times lead to more light
    • … though blurring of moving subjects
  • Together, determine the exposure
    • The amount of light allowed to enter the camera
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
image formation in a digital camera
Image Formation in a Digital Camera

+10V

Photon

+ + + + + +

+

CCD sensor

  • Array of sensors
    • Light-sensitive diodes that convert photons to electrons
    • Each cell corresponds to a picture element (pixel)
  • Sensor technologies
    • Charge Coupled Device (CCD)
    • Complementary Metal Oxide Semiconductor (CMOS)
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

slide13

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
    • E.g., eight bits for each color
      • With 28 = 256 values
      • Corresponding to intensity
    • 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: estimating pixels we do not know for certain
  • For a non-green cell, look at the neighboring green cells
    • And, interpolate the value
  • Accuracy of interpolation
    • Good in low-contrast areas
    • Poor with sharp edges (e.g., text)

Estimate “RGB” at the “G” cells from neighboring values

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
  • Common 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
lossy vs lossless compression
Lossy vs. Lossless Compression
  • Lossless
    • Only exploits redundancy in the data
    • So, the data can be reconstructed exactly
    • Necessary for most text documents (e.g., legal documents, computer programs, and books)
  • Lossy
    • Exploits both data redundancy and human perception
    • So, some of the information is lost forever
    • Acceptable for digital audio, images, and video
examples of lossless compression
Examples of Lossless Compression
  • Huffman encoding
    • Assign fewer bits to less-popular symbols
    • E.g., “a” occurs more often than “i”
    • … so encode “a” as “000” and “i” as “00111”
    • Efficient when probabilities vary widely
  • Run-length encoding
    • Identify repeated occurrences of the same symbol
    • Capture the symbol and the number of repetitions
    • E.g., “eeeeeee”  “@e7”
    • E.g., “eeeeetnnnnnn”  “@e5t@n6”
joint photographic experts group
Joint Photographic Experts Group
  • Lossy compression of images
    • Starts with an array of pixels in RGB format
      • With one number per pixel for each of the three colors
    • Outputs a smaller file with some loss in quality
    • Exploits both redundancy and human perception
      • Transforms the 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
  • Digital cameras
    • Light and a optical lens
    • Charge and electronic devices
    • Pixels and a digital computer
  • Digital images
    • A two-dimensional array of pixels
    • Red, green, and blue intensities for each picture
  • Image compression
    • Raw images are very large
    • Compression reduces the image size substantially
    • By exploiting redundancy and human perception