1. From Analog to Digital

2. What are Digital Images?
Electronic snapshots taken of a scene or scanned from documents
samples 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

3. CUL Bias on Image Capture:
Create rich images that are useful over time in the most cost-effective manner.
Set conversion requirements greater than immediate application
Promote reuse of content
Enable sharing of comparable and trusted resources across disciplines, users, and institutions

4. Why Rich Digital Masters?
Preservation
Original may only withstand one scan
Maintenance of digital files
Cost
One scan may be all that is affordable
Conversion costs dwarfed by other costs
Access
Many from one
The richer the file, the better the derivative in terms of quality and processibility

5. How to determine what’s good enough?
Connoisseurship of document attributes
Identify key information content
Objectively characterize or measure attributes: size, detail, tone, and color
Appreciate imaging factors affecting quality and cost
Translate between analog and digital
Equate measurements to digital equivalencies and corresponding metrics, e.g., detail size  resolution  MTF

6. CUL’s Approach to Imaging: “No More, No Less”
desired point of capture
image quality and utility
Image requirements and cost

7. Digital Image Quality is Governed By:
resolution and threshold
bit depth
color management
image enhancement
compression and file format
system performance

8. Resolution Determined by number of pixels used to represent the image
Increasing resolution increases level of detail captured and geometrically increases file size
zoom in

9. Effects of Resolution
600 dpi
300 dpi
200 dpi

10. Threshold Setting in Bitonal Scanning
defines the point on a scale from 0 to 255 at which gray values will be interpreted either as black or white

11. Effects of Threshold
threshold = 60
threshold = 100

12. Bit Depth
Determined by the number of binary digits (bits) used to represent each pixel
8-bit
24-bit
1-bit

13. Bit Depth
increasing bit depth increases the level of gray or color information that can be represented and arithmetically increases file size
Bit depth, dynamic range, and color appearance
Add a slide on binary arithmetic in the “real” presentation, with reality check.

14. Utilizing Sufficient Bit-Depth
3-bit gray
8-bit gray

15. Utilizing Sufficient Bit Depth
8-bit color
24-bit color

16. Bit Depth vs. Dynamic Range
The range of tonal difference between lightest light and the darkest dark

17. Mapping Tones Correctly: Use of Histograms

18. Representing Color Appearance
Balanced Color
Color Shift Towards Red

19. Image Enhancement Image editing to modify or improve an image
filters (brightness, contrast, sharpness, blur)
tone and color correction
Use raises concerns about fidelity and authenticity

20. Effects of Filters
no filters used
maximum enhancement

21. Image Editing

22. Compression
reduces file size for processing, storage, transmission, and display
image quality may be affected by the compression techniques used and the level of compression applied

23. Compression Variables
lossless versus lossy compression
proprietary vs. open schemes
level of industry support
bitonal vs. gray/color
see attributes of common compression techniques at:

24. Effects of JPEG Compression
300 dpi, 8-bit grayscale
uncompressed TIFF
JPEG 18.5:1 compression

25. Compression /File Format Comparison
GIF (lossless)
File Size: 60 KB
JPEG (lossy)
File Size: 49 KB
images courtesy of Edison Papers

26. File Formats
Consist of both the bits that comprise image information and header information on how to read and interpret the file
Image quality affected by format support for:
Bit depth
Compression techniques
Color management
Hardware,software, and network support
See common file formats chart at:

27. Equipment used and its performance over time
scanners with same stated functionality can produce different results
Factors affecting image quality:
Optical, mechanical, and sensing components
Calibration
Age of equipment
Environment

28. Variations in Image Quality due to Scanner Performance
300 dpi, scanner A
300 dpi, scanner B

29. Correlating Document Attributes to Image Requirements
Example: Determining Resolution Requirements
What’s you finest feature?
What’s your quality requirement?
What’s your imaging approach?

30. Case Study: Brittle Books
Variables: feature size, quality, imaging approach
fixed metric: smallest lower case letter
QI values: 8(excellent), 5 (good), 3.6 (marginal)
Resolution key to text capture, e.g., dpi
Bitonal QI formula for text
DPI = 3QI/.039h
600 dpi 1-bit capture adequately preserves informational content of cleanly produced text and supports image processing (e.g., OCR)

33. Textual Documents May Require Tonal Capture
Pages badly stained
Pages exhibit low contrast between text and background
Fine features not fully resolved
Pages contain complex graphics, color or important contextual information
Gray/color QI formula for text
Dpi = 2QI/.039h

34. Determining Resolution Requirements beyond Text
Stroke width
Finest scale
Visual perception

35. Defining Detail as Stroke
Edge-based representations
Variables: feature size, quality, imaging approach
fixed metric: width of finest line, stroke, dot, or marking
QI values based on sampling frequency: 2(excellent), 1.5 (good), 1(marginal)
Resolution and bit depth key to quality

36. Defining Detail as Stroke
QI formulas for stroke
Gray/color: dpi = QI/.039w
QI = dpi x .039w
Bitonal: dpi = (1.5QI)/.039w

37. Adequately Rendered Stroke

38. Inadequately Rendered Stroke

39. Example
Manuscript page with finest significant stroke measuring .2mm, which must be fully captured
Gray/color: dpi = QI/.039w
QI=2, w=.2
Dpi = 2/[.039(.2)] = 256 dpi

40. Defining Detail as Scale
Smallest significant scale or repeatable pattern, e.g., knots in a rug vs. strands in the thread
Can result in very high resolution requirements (e.g., photographs, book illustrations)
Halftones: scan at 4 times the screen ruling, utilize special descreen/rescreening, or scan in grayscale (400 dpi is a good default)

41. Halftone Scanned at 150 DPI

42. Halftone Scanned at 400 DPI

43. Defining Detail Based on Visual Perception
Human eye can detect details approximately 1/215 inch wide
Finer details are optically averaged
Using two pixel rule, visual perception requirements met at 430 dpi
Illustrated Book Study: 400 dpi grayscale recommendation

44. y.

45. Detail Represented at 400 dpi

46. Translating Between Digital Resolution and Scanner Performance
Detail capture governed by resolution, threshold, bit-depth, and system performance
Sampling resolution (DPI) is not a true indicator of image quality, although it may suffice for scanning in the dpi range
Monitor Emerging System Performance Metrics (e.g., MTF)

47. Aligning Document Attributes with Digital Requirements
Identify key document attributes
Tone, color, and detail
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

48. Aligning Document Attributes with Digital Requirements
Calibrate scanner with targets and software
Calibrate the rest of the system
Control lighting and environment
Scan appropriate targets with documents
Evaluate images against originals

49. Aligning Document Attributes with Digital Requirements
Minimize post-processing in the master image
Save in TIFF; avoid lossy compression
Maintain scanning metadata
Monitor emerging image quality metrics

50. One Size Does Not Fit All!
Different document types will require different scanning equipment and processes
The more complex the document, the higher the conversion/access requirements
Scan the original whenever possible
No standards for image conversion: guidance rather than guidelines
Notion of long-term utility and cross-institutional resources gaining ground