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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


Digital photography l.jpg
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 l.jpg
Image Formation

Digital Camera

Film

Eye


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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 l.jpg
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 l.jpg
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 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


Slide13 l.jpg

The 2272 x 1704hand

The 320 x 240hand


Representing color l.jpg
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 l.jpg
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: 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 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

  • Common sizes

    • 640 x 480: 1 Megabyte

    • 800 x 600: 1.5 Megabytes

    • 1600 x 1200: 6 Megabytes


Compression l.jpg
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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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 l.jpg
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”  “@[email protected]


Joint photographic experts group l.jpg
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 l.jpg
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


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