Digitizing Data

2. Digitizing Data • Text is easy • What about multimedia? • Photos, audio, and video • Same principles

3. Color and the Mystery of Light • Color image • Grid of pixels • Pixel is formed from three primary colors • RGB

4. Showing Colors • Colors formed by using 3 intensities of primaries • Full intensity red, green, or blue • Full intensity of red, green, and blue? • No intensity of any color? • Other combinations

5. LCD Display Technology • Close-up of white arrow pointer • Note subpixels

6. Black and White Colors • Intensity of light color stored in byte • Needs 3 bytes/pixel • Smallest intensity is 0000 0000 • Decimal? • Largest value is 1111 1111 • Decimal?

7. Black and White Colors • Black is absence of light: • 0000 0000 0000 0000 0000 0000 • White is full intensity of each color: • 1111 1111 1111 1111 1111 1111

8. Color Intensities • Consider blue (0000 0000 0000 0000 1111 1111) • 8 bits have position values • To cut intensity in half

9. Color Intensities

10. Decimal to Binary • How to convert decimal to binary? • Look for powers of 2 and subtract • E.g.: Convert 365 to binary

11. Lighten Up: Changing Color by Addition What color does this represent? 1100 10001100 10001100 1000 It’s RGB (200, 200, 200), a grey #C8 C8C8 in hex All grays of form (x, x, x)

12. To Increase Intensity: Add in Binary Increase common value to lighten E.g., add 0001 0000 (decimal 16) to each color 1101 10001101 10001101 1000 RGB (216,216,216)

13. Lightening Adding another 16… 1101 1000 + 0001 0000 ----------------- Check using decimal!

14. Lighter Still: Adding with Carry Digits Binary addition is similar to decimal addition Work from right to left 0 + 0 = 0, 0 + 1 = 1, 1 + 0 = 1 No carry 1 + 1 = 0 Carry of 1

15. Binary Addition

16. Binary Addition

17. Binary Addition

19. Computing on Representations • Example: changing the brightness and contrast of a photo

20. Brightness and Contrast • Brightness • How close to white • Contrast • Difference b/w darkest and lightest portions of image • Photo manipulation software often gives values of pixels in a Levels graph

21. Levels Graph • 0 percent is black point (0, 0, 0) • 100 percent is white point (ff, ff, ff) • Midpoint of pixel range is gamma point

22. Brightness • Shift pixels closer to white • Add 16 to each pixel • E.g.: (197, 197, 197) => (213, 213, 213)

23. Contrast • Scale pixel range • Stretch toward right • Add to each pixel, but • add a smaller amount for dark pixels • add a larger amount for light pixels

24. New Levels Graph

25. Adding Color • Color => (x, y, z), all 3 differ • Example • Colorize image of moon

26. Making the Moon Orange Tint white regions Pick a shade of orange, say (255,213,132) Tint light gray Red byte: leave unchanged Green byte: reduce green slightly (subtract 42) Blue byte: reduce blue significantly (subtract 123)

28. Digitizing Sound • Vibrating object creates sound • Vibrations “push” air to form pressure wave • Wave vibrates our eardrums

29. Digitizing Sound • Intensity of push determines volume • Frequency (# of waves per second) of pushes determines pitch continuous (analog) representation of the wave

30. Analog to Digital • Need to digitize to bits • Use binary # for amplitude of wave • At what point do you measure? • Infinitely many possible

31. Analog to Digital • Sample at regular intervals • Samples/second is sampling rate

32. Nyquist Rule for Sampling • Sampling rate is key • Nyquist rule • Sampling rate must 2x highest frequency • Range of human hearing 20 Hz – 20 KHz • Digital audio sampling rate is 44.1 KHz

33. Digitizing Process

34. Digitizing Process • Recording (digitizing) process • Sound => mic • Signal sampled by ADC • Samples encoded in binary

35. Digitizing Process • Playing process • Numbers read by DAC • Electrical wave created by interpolation • Electrical signal => speaker

36. How Many Bits per Sample? • Perfect accuracy requires unlimited bits/sample • Must handle both +/- values • More bits => more accuracy

37. How Many Bits per Sample? • More bits yields a more accurate digitization • Digital audio uses 16 bits

38. Advantages of Digital Sound • Key advantage is ability to compute on representation • Remove noise • Compression • Lossless • Lossy • Frequencies outside our range

39. Advantages of Digital Sound • MP3 format • Allows for compression ratio > 10:1 • Another key advantage of digital representations is exact reproduction

40. Digital Images and Video • Image is grid of RGB pixels • Stored as linear sequence • Can take up a lot of space

41. Digital Images and Video • Example • 8 × 10 image scanned at 300 ppi • How many bytes to store? • Sending across 56 Kb/s phone connection requires how many minutes?

42. Image Compression • Typical monitor has fewer than 100 ppi • 9x space saving over 300 ppi • Still requires more than 5.5 min to send

43. Image Compression • Compression changes rep to use fewer bits • Example: faxes • Faxes are a sequences of 0’s and 1’s • Use run-length encoding

44. Compression • Run-length encoding is “lossless” • Opposite is “lossy compression”

45. Compression • MP-3 • Lossy scheme • Highs and lows lost • JPG (or JPEG) • Lossy scheme for images • Exploits limits of human perception • Luminance sensitivity • Chrominance insensitivity

46. JPEG Compression • JPEG is capable of 10:1 compression w/o detectable loss of clarity

47. JPEG Compression • Ratios higher than 10:1 • Smaller files • Possible artifacts (pixelation)

48. MPEG Compression • MPEG used for video • Video is sequence of stills • Each image/frame is not seen for long

49. MPEG Compression • JPEG used to compress frames • “Interframe coherency” is used • MPEG compression only transmits “deltas” b/w frames • Results in significant compression

50. Optical Character Recognition • OCR • Converts images of characters to characters • Used by U.S.P.S. • Used in banking • Used in automatic license-plate recognition