1. HBCU-CUL Digital Imaging Workshop, November 2005
From Analog to Digital
Peter Hirtle
HBCU-CUL Digital Imaging Workshop II
July 31, 2007
(c) Cornell University Library 2005

2. Session Goal
Workshop participants will understand the issues involved in converting analog materials into digital formats for preservation and access.

3. Session Objectives
Overview the application of the scanning process in the digitization chain
In-depth presentation on the factors affecting image quality
Discuss the issues involved in presentation
Participate in “reality checks” on lessons learned

4. Learner Outcomes
Be able to Discuss issues involved in converting analog materials to digital images
Compare and Contrast three types of image scanning
Understand factors affecting image presentation

5. Other 3D images are tape-based, such as:
Analog to Digital
We’re scanning 2D images – (textual and graphical) & 3D images (photographic)
Other 3D images are tape-based, such as:
Video
Audio

6. What are Digital Images?
HBCU-CUL Digital Imaging Workshop, November 2005
What are Digital Images?
“electronic photographs” created through scanning or digital photography
Sampled and mapped as a grid of dots or picture elements (pixels)
pixel assigned a tonal value (black, white, grays, colors), represented in binary code
code stored or reduced (compressed)
read and interpreted to create analog version
(c) Cornell University Library 2005

7. HBCU-CUL Digital Imaging Workshop, November 2005
Digital Image
Also known as a raster or bitmap image
(c) Cornell University Library 2005

8. Three types of scanning
HBCU-CUL Digital Imaging Workshop, November 2005
Three types of scanning
BITONAL GRAYSCALE COLOR
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9. HBCU-CUL Digital Imaging Workshop, November 2005
Bitonal Scanning
Information is presented as either black or white
Gray shades simulated by clustering black dots
Suitable for printed materials
(c) Cornell University Library 2005

10. HBCU-CUL Digital Imaging Workshop, November 2005
Grayscale Scanning
Shades of gray represented; normally
8 bits per pixel
Suitable for manuscripts, photographs, halftones, and stained documents
(c) Cornell University Library 2005

11. Bitonal vs. Grayscale Capture of Stained Manuscript
HBCU-CUL Digital Imaging Workshop, November 2005
Bitonal vs. Grayscale Capture of Stained Manuscript
Bitonal scan
Grayscale scan
(c) Cornell University Library 2005

12. HBCU-CUL Digital Imaging Workshop, November 2005
Color Scanning
Full color range; normally 24 bits per pixel
Suitable for documents with significant color information, such as photographs, works of art
Helpful in determining a document’s age, physical condition, or previous use
(c) Cornell University Library 2005

13. Digital Image Quality Affected By
HBCU-CUL Digital Imaging Workshop, November 2005
Digital Image Quality Affected By
Document attributes
Resolution and threshold
Bit depth and dynamic range
Image enhancement
Compression/format
Equipment and performance over time
Operator judgment and care
(c) Cornell University Library 2005

14. Important Document Attributes
HBCU-CUL Digital Imaging Workshop, November 2005
Important Document Attributes
Physical type, size, and presentation
Physical condition
Document type
The Big Three
Detail, Tone, Color
(c) Cornell University Library 2005

15. HBCU-CUL Digital Imaging Workshop, November 2005
Halftone: 1-bit image at 400 dpi
Scanned from Original
On H-P ScanJet 3c
Scanned from 2N Film
On Sunrise SRI 50
(c) Cornell University Library 2005

16. HBCU-CUL Digital Imaging Workshop, November 2005
                                                                                            
HBCU-CUL Digital Imaging Workshop, November 2005
Matching Informational Content to Scanning
Approach: Quality vs. File Size and Cost
600 dpi 1-bit
2.9 Mb file
400dpi 8-bit
10.3 Mb file
400 dpi 24-bit
30.9 Mb file
                                                                                           
(c) Cornell University Library 2005

17. HBCU-CUL Digital Imaging Workshop, November 2005
Resolution
Determined by number of pixels used to represent the image
expressed in dots per square inch (dpi)
zoom in
(c) Cornell University Library 2005

18. HBCU-CUL Digital Imaging Workshop, November 2005
Resolution
100 dpi = 1002 (100 x 100) or 10,000 dots
200 dpi = 2002 or 40,000 dots
400 dpi = 4002 or 160,000 dots
increasing resolution increases detail captured and geometrically increases the file size
(c) Cornell University Library 2005

19. HBCU-CUL Digital Imaging Workshop, November 2005
Effects of Resolution
600 dpi
300 dpi
200 dpi
(c) Cornell University Library 2005

20. Threshold Setting in Bitonal Scanning
HBCU-CUL Digital Imaging Workshop, November 2005
Threshold Setting in Bitonal Scanning
defines the point on a scale from 0 (black) to 255 (white) at which gray values will be interpreted either as black or white
255
dark
gray
light
gray
black
white
(c) Cornell University Library 2005

21. HBCU-CUL Digital Imaging Workshop, November 2005
Effects of Threshold
threshold = 60
threshold = 100
(c) Cornell University Library 2005

22. Defining Detail in Text
HBCU-CUL Digital Imaging Workshop, November 2005
Defining Detail in Text
Fixed metric: smallest lower case letter
Variables: quality, resolution, feature size
Bitonal QI formula for text
dpi = 3QI/.039h
QI values: 8(excellent), 5(good), 3.6(marginal), 3(barely legible)
Grayscale/Color QI formula for text
dpi = 2QI/.039h
(c) Cornell University Library 2005

23. HBCU-CUL Digital Imaging Workshop, November 2005
Example
Text page with smallest character measuring 1mm, which must be fully captured (QI=8, h= 1)
Bitonal Scanning: dpi = 3QI/.039h
dpi = 3(8)/[.039(1)] = 615 dpi
Grayscale: dpi = 2QI/.039h
dpi = 2(8)/[.039(1)] = 410 dpi
(c) Cornell University Library 2005

24. HBCU-CUL Digital Imaging Workshop, November 2005
(c) Cornell University Library 2005

25. HBCU-CUL Digital Imaging Workshop, November 2005
Bit Depth
Determined by the number of binary digits (bits) used to represent each pixel
8-bit
24-bit
1-bit
(c) Cornell University Library 2005

26. Bitonal scanning has a bit-depth of 1
HBCU-CUL Digital Imaging Workshop, November 2005
Bitonal scanning has a bit-depth of 1
each pixel represented by one bit, with a tonal range of 2:
0 = black
1 = white
(c) Cornell University Library 2005

27. HBCU-CUL Digital Imaging Workshop, November 2005
Binary Calculations
21 = 2
22 = 4
23 = 8
24 = 16
28 = 256
210 = 1024
212 = 4096
224 = 16.7 million
(c) Cornell University Library 2005

28. HBCU-CUL Digital Imaging Workshop, November 2005
Bit Depth
Grayscale is typically 8 bits or more, representing 256 (28) levels
Color is 24 bits or more , representing 16.7 million (224) levels
example: 8-bit grayscale pixel
= black
= white
(c) Cornell University Library 2005

29. HBCU-CUL Digital Imaging Workshop, November 2005
Bit Depth
increasing bit depth increases the levels of gray or color that can be represented and arithmetically increases file size
affects resolution requirements
Add a slide on binary arithmetic in the “real” presentation, with reality check.
(c) Cornell University Library 2005

30. Effects of Grayscale on Image Quality
HBCU-CUL Digital Imaging Workshop, November 2005
Effects of Grayscale on Image Quality
3-bit gray
8-bit gray
(c) Cornell University Library 2005

31. HBCU-CUL Digital Imaging Workshop, November 2005
Tonal Reproduction
Dynamic range: the range of tonal difference between lightest light and darkest dark
The higher the range, the greater the number of potential shades
Tone distribution as important as dynamic range
(c) Cornell University Library 2005

32. HBCU-CUL Digital Imaging Workshop, November 2005
Comparing Key Types
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33. HBCU-CUL Digital Imaging Workshop, November 2005
Mapping Tones Correctly: Use of Histograms
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34. HBCU-CUL Digital Imaging Workshop, November 2005
Color Appearance
Difficult to evaluate
Hue, saturation, brightness
Translating between analog and digital, between color spaces, between reflected and transmitted light
(c) Cornell University Library 2005

35. HBCU-CUL Digital Imaging Workshop, November 2005
Representing Color Appearance
Balanced Color
Color Shift Towards Red
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36. HBCU-CUL Digital Imaging Workshop, November 2005
Color Cast
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37. Color Imaging and Tone Distribution
HBCU-CUL Digital Imaging Workshop, November 2005
Color Imaging and Tone Distribution
Limited tones evident in highlights and shadows
Balanced tonal distribution
(c) Cornell University Library 2005

38. Image Processing and Enhancement
HBCU-CUL Digital Imaging Workshop, November 2005
Image Processing and Enhancement
Image editing to modify or improve an image
filters (brightness, contrast, sharpness, blur)
file size reduction (scaling)
tone and color correction
(c) Cornell University Library 2005

39. HBCU-CUL Digital Imaging Workshop, November 2005
Effects of Filters
no filters used
maximum enhancement
(c) Cornell University Library 2005

40. HBCU-CUL Digital Imaging Workshop, November 2005
Compression/format
image quality may be affected by the compression technique used to reduce file size
Image quality affected by format support for:
Bit depth
Compression techniques
Color management
(c) Cornell University Library 2005

41. HBCU-CUL Digital Imaging Workshop, November 2005
Compression /File Format Comparison
GIF (lossless)
File Size: 60 KB
JPEG (lossy)
File Size: 49 KB
images courtesy of Edison Papers
(c) Cornell University Library 2005

42. Equipment used and its performance over time
HBCU-CUL Digital Imaging Workshop, November 2005
Equipment used and its performance over time
scanners offer wide range of capabilities to capture detail, tone, dynamic range, and color
scanners with same stated functionality can produce different results
calibration, age of equipment, and environment are also factors
(c) Cornell University Library 2005

43. HBCU-CUL Digital Imaging Workshop, November 2005
Variations in Image Quality due to Scanner Performance
300 dpi, scanner A
300 dpi, scanner B
(c) Cornell University Library 2005

44. HBCU-CUL Digital Imaging Workshop, November 2005
Image Capture:
Creating digital files rich enough to be useful over time in the most cost- effective manner.
(c) Cornell University Library 2005

45. Why Create High Quality Images?
HBCU-CUL Digital Imaging Workshop, November 2005
Why Create High Quality Images?
preservation
original may only withstand one scan
cost
one scan may be all that is affordable
access
one scan can be used to derive multiple images
(c) Cornell University Library 2005

46. HBCU-CUL Digital Imaging Workshop, November 2005
Calculating File Size
F file size = height x width x bit depth x dpi2 8
(c) Cornell University Library 2005

47. File Size Naming Conventions
HBCU-CUL Digital Imaging Workshop, November 2005
File Size Naming Conventions
Represented in bytes
1 byte = 8 bits
1Kb ~ 1,000 bytes
1Mb ~ 1,000Kb
1Gb ~ 1,000Mb
(c) Cornell University Library 2005

48. Example: grayscale image
HBCU-CUL Digital Imaging Workshop, November 2005
Example: grayscale image
file file size = height x width x bit depth x dpi2 8 7”
file size = 10 x 7 x 8 x 3002 8
file size = 6,300,000 bytes
= 6.3 megabytes
10”
300 dpi, 8-bit
(c) Cornell University Library 2005

49. HBCU-CUL Digital Imaging Workshop, November 2005
Example: Bitonal Image
800
file size = height x width x bit depth x dpi2 8
file size = 10 x 8 x 1x 2002 8
file size = 3,200,000 bytes
= 3.2 megabytes
10” 8”
200 dpi , 1 bit
(c) Cornell University Library 2005

50. HBCU-CUL Digital Imaging Workshop, November 2005
Reality Check 1
Determine the file size of a letter size page captured bitonally at 200 dpi.
(c) Cornell University Library 2005

51. HBCU-CUL Digital Imaging Workshop, November 2005
Answer
(8.5 x 11 x 2002)/8 = 468Kb or .47Mb
(c) Cornell University Library 2005

52. HBCU-CUL Digital Imaging Workshop, November 2005
Reality Check 2
Determine the file size of an 8” x 10” photograph scanned in color at 200 dpi.
(c) Cornell University Library 2005

53. HBCU-CUL Digital Imaging Workshop, November 2005
Answer
(8 x 10 x 24 x 2002) = 9.6 million bytes or Mb
(c) Cornell University Library 2005

54. Estimating File Size for Compressed Images
HBCU-CUL Digital Imaging Workshop, November 2005
Estimating File Size for Compressed Images
Compressed file size = file size
level of compression
Example: Color photo compressed 20:1
9.6Mb =.48Mb
(c) Cornell University Library 2005

55. Aligning Document Attributes with Digital Requirements
HBCU-CUL Digital Imaging Workshop, November 2005
Aligning Document Attributes with Digital Requirements
Identify key document attributes
Detail, tone, color
Characterize them, if possible through objective measurements
Determine quality requirements and tolerance levels
Translate between analog and digital and between scanning requirements and scanning performance
(c) Cornell University Library 2005

56. Aligning Document Attributes with Digital Requirements
HBCU-CUL Digital Imaging Workshop, November 2005
Aligning Document Attributes with Digital Requirements
Calibrate scanner, calibrate the rest of the system
Control lighting and environment
Objective and Subjective Evaluation: targets and software; evaluating images against the originals
(c) Cornell University Library 2005

57. Aligning Document Attributes with Digital Requirements
HBCU-CUL Digital Imaging Workshop, November 2005
Aligning Document Attributes with Digital Requirements
Minimize post-processing in the master image
Save in TIFF; use RGB for color; and avoid lossy compression
Maintain scanning metadata
(c) Cornell University Library 2005

58. One Size Does Not Fit All!
HBCU-CUL Digital Imaging Workshop, November 2005
One Size Does Not Fit All!
different document types will require different scanning processes
the more complex the document, the higher the conversion/access requirements, the larger the file, and the greater the expense
scan the original whenever possible
Follow project recommendations
(c) Cornell University Library 2005