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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 l.jpg

Digital Cameras

Engineering Math Physics (EMP)

Jennifer Rexford

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


Image transmission over wireless networks l.jpg
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 l.jpg
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 l.jpg
Image Formation

Digital Camera

Film

Eye


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Image Formation in a Pinhole Camera

  • Light enters a darkened chamber through pinhole opening and forms an image on the further surface


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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


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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


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Digital Photography

  • Digital photography is an electronic process

  • Only widely available in the last ten years

  • Digital cameras now surpass film cameras in sales


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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)


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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


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Sensor Array: Image Sampling

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


Sensor array reading out the pixels l.jpg
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 l.jpg
More Pixels Mean More Detail

1280 x 960

1600 x 1400

640 x 480


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The 2272 x 1704hand

The 320 x 240hand


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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 l.jpg
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


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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


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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


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Separate Sensors Per Color

  • Expensive cameras

    • A prism to split the light into three colors

    • Three CCD arrays, one per RGB color


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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


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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


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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


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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 l.jpg
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


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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


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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


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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


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