1. © 1999 Rochester Institute of Technology Introduction to Digital Imaging

2. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT What is a Digital Image? u Just an array of numbers! 50 44 23 31 38 52 75 52 29 09 15 08 38 98 53 52 08 07 12 15 24 30 51 52 10 31 14 38 32 36 53 67 14 33 38 45 53 70 69 40 36 44 58 63 47 53 35 26 68 76 74 76 55 47 38 35 69 68 63 74 50 42 35 32

3. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Pixel u Each picture element in the array is called a pixel. u Each pixel is represented by a number. 50 44 23 31 38 52 75 52 29 09 15 08 38 98 53 52 08 07 12 15 24 30 51 52 10 31 14 38 32 36 53 67 14 33 38 45 53 70 69 40 36 44 58 63 47 53 35 26 68 76 74 76 55 47 38 35 69 68 63 74 50 42 35 32 32 The “32” could represent a color, or a gray level

4. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Capturing Images u How do we capture images digitally? 0 1 0 1 0 1 0 1 0 1

5. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Traditional vs. Digital Photography Chemical processing Detector: Photographic film Digital processing Detector: Electronic sensor (CCD)

6. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Goal of Charge Coupled Device (CCD) u Capture electrons formed by interaction of photons with the silicon u Measure the electrons from each picture element as a voltage CCD Photons Electronic Signal

7. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Charge Coupled Device (CCD) u CCD chip replaces silver halide film u No wet chemistry processing u Image available for immediate feedback

8. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Magnified View of a CCD Array Individual pixel element Close-up of a CCD Imaging Array CCD

9. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Charge Coupled Device (CCD) u Lens projects image onto the CCD u CCD ‘samples’ the image, creating different voltages based on the amount of light at each pixel u Voltages are converted to digital signals and stored

10. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Spatial Sampling u When a scene is imaged onto the CCD by the lens, the continuous image is ‘sampled’ and divided into discrete picture elements, or ‘pixels’ Scene Grid over sceneSpatially sampled scene

11. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Quantization u The spatially sampled image is then converted into an ordered set of integers (0, 1, 2, 3, …) according to how much light fell on each element Spatially sampled scene 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 40 0 0 25 40 25 40 25 40 25 40 64 97 150 97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Numerical representation

12. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Basic structure of CCD Divided into small elements called pixels (picture elements). preamplifier ImageCaptureArea Shift Register Voltage out Columns Rows

13. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Fundamentals: Digital Images u A digital image is an ordered collection of numbers u To be useful, the collection of numbers must be in a known, pre-defined format. u The rules of English let us ‘parse’ letters into words 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 Introductiontodigitalimagingforgearupstudentsfromnewyork

14. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Fundamentals: Digital Images u There is no ‘universal rule’ to decode the string of 0s and 1s in a digital file into an image 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 1 0 u Image Formats provide the definitions that allow a string of numbers to be understood as an image

15. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Fundamentals: Digital Images u Once we know the format, each number can be read and used to describe the lightness or color of a specific picture element (“pixel”) 1 1 1 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 0 0 0 1 1 0

16. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Image quality factors u Two major factors which determine image quality are: u Bit depth -- controlled by number of colors or grey levels allocated for each pixel u Spatial resolution -- controlled by spatial sampling. u Increasing either of these factors results in a larger image file size, which requires more storage space and more processing/display time.

17. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Fundamentals: Digital Images u The simplest kind of digital image is known as a “binary image” because the image contains only two ‘colors’ - white and black

18. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Binary Images u Because binary images contain only two colors, we can encode the image using just two numbers, for example: u 0 = black u 1 = white 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0

19. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 2 shades of gray

20. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 4 shades of gray

21. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 8 shades of gray

22. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 16 shades of gray

23. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 32 shades of gray

24. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT 256 shades of gray

25. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Bit depth: bits per pixel u The number of possible gray levels is controlled by the number of bits/pixel, or the ‘bit depth’ of the image gray levels 248163264128256 Bit depth; bits/pixel 1 2 3 4 5 6 7 8

26. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Digital images: Fundamentals 39 56 45 75 62 99 64 101 228178106193 18314384162 u A digital image is an ‘ordered array of numbers’ u Each pixel (picture element) in a grayscale digital image is a number that describe the pixel’s lightness (e.g., 0 = black 255 = white)

27. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT

28. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT

29. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Color Images u In most cases, we also want to capture color information u The way that we capture, store, view, and print color digital images is based on the way that humans perceive color

30. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Color Perception u The eyes have three different kinds of color receptors (‘cones’); one type each for blue, green, and red light. u Color perception is based on how much light is detected by each of the three ‘primary’ cone types (red, green, and blue)

31. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Color Perception u Because we have three kinds of cones, every color that we can see can be made up by combining red, green, and blue light - the three “additive primaries”

32. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Additive Color Mixing: u Mixing the three additive primaries together is known as “additive mixing” to distinguish it from mixing paints or dyes (“subtractive mixing”)

33. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Additive Color Mixing: u Remember that we are discussing “additive color mixing.” The mixing happens in the visual system, not on the screen. u You can verify this by examining a TV or computer screen at high magnification. Color monitors and LCD displays only make red, green, and blue light. All other colors are synthesized in the visual system.

34. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Additive Color Mixing: u By recording and playing back the amount of Red, Green, and Blue at each pixel, a digital camera can capture the colors in a scene.

35. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT

36. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images u Each one of the color images (‘planes’) is like a grayscale image, but is displayed in R, G, or B = u The most straightforward way to capture a color image is to capture three images; one to record how much red is at each point, another for the green, and a third for the blue.

37. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images u To capture a color image we record how much red, green, and blue light there is at each pixel. u To view the image, we use a display (monitor or print) to reproduce the color mixture we captured. Q) How many different colors can a display produce? A) It depends on how many bits per pixel we’ve got. For a system with 8 bits/pixel in each of the red, green, and blue (a ‘24-bit image’):

38. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images: 24-bit color u Every pixel in each of the three 8-bit color planes can have 256 different values (0-255) u If we start with just the blue image plane, we can make 256 different “colors of blue” 0 255

39. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images: 24-bit color u Every pixel in each of the three 8-bit color planes can have 256 different values (0-255) u If we start with just the blue image plane, we can make 256 different “colors of blue” u If we add red (which alone gives us 256 different reds): 0255 0

40. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images: 24-bit color u Every pixel in each of the three 8-bit color planes can have 256 different values (0-255) u If we start with just the blue image plane, we can make 256 different “colors of blue” u If we add red (which alone gives us 256 different reds): u We can make 256 x 256 = 65,536 combination colors because for every one of the 256 reds, we can have 256 blues. 0255 0

41. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images: 24-bit color for each one u When we have all three colors together, there are 256 possible values of green for each one of the 65,536 combinations of red and blue: u 256 x 256 x 256 = 16,777,216 (“> 16.7 million colors”)

42. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT RGB Color Images: 24-bit color u The numbers stored for each pixel in a color image contain the color of that pixel

43. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Color Image = Red + Green + Blue = u In a 24-bit image, each pixel has R, G, & B values u When viewed on a color display, the three images are combined to make the color image. 212 100 139196 163 75 113149 37 44 6372 95 118 155170 189 162 38 41 60 8278 182 161 50 43 57 6863

44. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT

45. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT

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47. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Bit Depth: Review u The color, or value of each pixel in an image is specified by a string of binary digits, or bits u The more bits available for each pixel, the greater the number of possible values each pixel can show: bits/pixelvalues 1 2 8 256 24 16,777,216

48. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Image quality factors u Two major factors which determine image quality are: u Bit depth -- controlled by number of colors or grey levels allocated for each pixel u Spatial resolution -- controlled by spatial sampling. u Increasing either of these factors results in a larger image file size, which requires more storage space and more processing/display time.

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62. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT File Size Calculation 100 pixels Bit depth = 8 bits per pixel (256 gray levels) File size (in bits) = Height x Width x Bit Depth 100 x 100 x 8 bits/pixel = 80,000 bits/image 80,000 bits or 10,000 bytes u How much memory is necessary to store an image that is 100 x 100 pixels with 8 bits/pixel?

63. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT File Size Calculation 1280 pixels 960 pixels Bit depth = 24 bits per pixel (RGB color) File size (in bits) = Height x Width x Bit Depth 960 x 1280 x 8 bits/pixel = 29,491,200 bits/image 29,491,200 bits = 3,686,400 bytes = 3.5 MB u How much memory is necessary to store an image that is 1280 x 960 pixels with 24 bits/pixel?

64. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Computer Memory & Storage u Unlike analog instruments (and people) that work with continuously variable signals, computers are inherently binary systems u Instead of ten distinct values (a decimal system), computers use only two values, e.g., u RAM memory: 0 or +5 volts u Disk drives: N or S magnetization u CD/DVD-ROM: ‘land’ or ‘pit’ u By convention, binary digits (bits) are labeled ‘0’ & ‘1’

65. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Binary Arithmetic u In binary arithmetic, we can only count from 0 to 1 with a single bit, giving two different values. 0101 0101 binary decimal 1 bit

66. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Binary Arithmetic u In binary arithmetic, we can only count from 0 to 1 with a single bit, giving two different values. u To get more than two values, we have to increase the number of bits. With two bits it is possible to count from 0 through 3 (decimal), giving four different values. 0101 0101 binary decimal 00 01 10 11 01230123 1 bit 2 bits

67. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Binary Arithmetic u Each added bit allows us to double the number of values we can represent with a binary number. u The number of values that can be represented is given by 2 N bits e.g., 4 bits provides 2 4 = 16 different values u A bit is a value, a position, and an amount of information 0 1 10 11 100 101 110 111 1000 1001 1010 1011 1100 1101 1110 1111 10000... binary 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16... decimal 1 bit 2 bits 3 bits 4 bits

68. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Computer Memory & Storage u Because of the internal design of early computers, 8 bits were grouped together and called a ‘byte’ 8 bits  1 byte u One byte can represent any one of ( 2 8 = 256) different values; 00000000  11111111 (binary) 0  255 (decimal)

69. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Computer Memory & Storage u 1 bit (‘binary digit’) u 1 byte = 8 bits u 1 kilobyte (KB) = 1,024 bytes (2 10 = 1024) (  1,000 bytes) u 1 megabyte (MB) = 1,048,576 bytes ( 2 20 ) (  1,000,000 bytes) u 1 gigabyte (GB) = 1,073,741,824 bytes ( 2 30 ) (  1,000,000,000 bytes)