1 / 28

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

omer
Download Presentation

Digital Cameras

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related