733667-001-011-0118

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AI Summary

This document outlines the guidelines and criteria for digitizing still images as part of the Federal Agencies Digitization Initiative. It emphasizes the importance of establishing quantitative metrics for image quality and provides a taxonomy for evaluating digital imaging performance.

Key Findings

- Establishes quantitative guidelines for digitization based on derivative metrics. - Differentiates between Primary, Secondary, and Tertiary measures of image quality. - Includes a taxonomy of digital imaging performance and related failure causes. - Provides a link to a PowerPoint primer on imaging science.

OCR Text

Federal Agencies Digitization Initiative Still Image Working Group — August 2010 Future work of the Still Image Working Group will rely on the information in these tables to establish quantitative guidelines using the described derivative metrics and evaluation criteria. The actual values that will be inserted into specific imaging guidelines will depend on the content to be digitized and the objectives for digitization. Graphical symbols used in the row labeled “Evaluative Criteria (wnits)” indicate Primary, Secondary and Tertiary measures (see tables below). These have meaning both across and within metrics. Across the metrics or image characteristics, they indicate the relative importance as a factor of image quality; from the highest (Primary) to the lowest (Tertiary). The same concept applies within the measurement for a given metric. Taking SFR as an example, Max SFR gain is suggested as the Primary Measure under Sharpening, and Sign of SFR slope as a Secondary Measure. There are also two additional informational tiers included in the table. One of these provides a listing of related descriptive terms that may be more commonly known to users. The bottom-most tier provides a list of possible causes of failure related to a particular metric. A a short primer and overview on imaging science is available as a Powerpoint at http://digitizationguidelines.gov/stillimages/presentations.html Part 1 - Taxonomy of Digital Imaging Performance See subsequent pages for information on definitions, candidate evaluation criteria, related descriptive terms, and failure causes : : signal g Noise : 2 CF 28 OE Te) ic Di M : : aati ( ee = a eres ianil Geometric Distortion 5 Function) Response) (Noise Power Spectrum) Total Noise HCL . 3 = Co Temporal | Fixed pattern | __Naise ‘ 3 8 3 < : in| | ® 3 2/2 - | 3 aS Ue aare 8a alll Uap e asi ees sk Sl's| x Disigig | S/S/8 |g 8 e iS Se Sale| £ (8 §ele] P/ee lass gy aislal“ 3| § E/E; & S/F gels 5 is 8 : &| 8| €/ Ss |e =| § E A: 28s) a)" |a| lal : 2/8 | 8 a * While imaging noise is generally considered to be of a random or stochastic granular nature (e.g., photographic film grain), it can actually take many forms. We have chosen to categorize it in both by its deterministic and stochastic behaviors. Technical Guidelines for Digitizing Cultural Heritage Materials: Creation of Raster Image Master Files 8

Metadata

Agency
—
Classification
UNCLASSIFIED
Department
National Archives and Records Administration
Confidence85
Credibility80

NARA Source

NAID
733667
File
733667-001-011-0118.jpg
Type
image/jpeg

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