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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 capture and compression Inner-workings of a digital camera Manipulating & transforming a matrix of pixels

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Digital Cameras

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  1. Digital Cameras Engineering Math Physics (EMP) Jennifer Rexford http://www.cs.princeton.edu/~jrex

  2. 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

  3. 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

  4. Image Formation Digital Camera Film Eye

  5. Image Formation in a Pinhole Camera • Light enters a darkened chamber through pinhole opening and forms an image on the further surface

  6. 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

  7. 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

  8. Digital Photography • Digital photography is an electronic process • Only widely available in the last ten years • Digital cameras now surpass film cameras in sales

  9. 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)

  10. 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

  11. Sensor Array: Image Sampling Pixel (Picture Element): single point in a graphic image

  12. 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

  13. Sensor Array: Reading Out the Pixels

  14. More Pixels Mean More Detail 1280 x 960 1600 x 1400 640 x 480

  15. The 2272 x 1704hand The 320 x 240hand

  16. 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

  17. 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

  18. 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

  19. 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

  20. Separate Sensors Per Color • Expensive cameras • A prism to split the light into three colors • Three CCD arrays, one per RGB color

  21. 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

  22. 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

  23. 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

  24. 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…

  25. 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

  26. 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

  27. 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

  28. 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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