Notice
This item was automatically migrated from a legacy system. It's data has not been checked and might not meet the quality criteria of the present system.
Bauer, S., & Becker, C. (2011). Automated Preservation: The Case of Digital Raw Photographs. In Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation (pp. 39–49). Springer. https://doi.org/10.1007/978-3-642-24826-9_9
E194-01 - Forschungsbereich Information und Software Engineering
-
Published in:
Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation
-
Date (published):
2011
-
Event name:
International Conference on Asian Digital Libraries (ICADL)
-
Event date:
27-Nov-2006 - 30-Nov-2006
-
Event place:
Kyoto, Japan, Non-EU
-
Number of Pages:
11
-
Publisher:
Springer, LNCS 7008
-
Peer reviewed:
Yes
-
Keywords:
digital preservation; automation; digital photography; image comparison
-
Abstract:
In digital preservation, a common approach for preservation actions is the migration to standardized formats. Full validation of the results of such conversion processes is required to ensure authenticity and trust. This process of quality assurance is a key obstacle to achieving scalability for large volumes of content. In this article, we address the quality assurance process for the preservatio...
In digital preservation, a common approach for preservation actions is the migration to standardized formats. Full validation of the results of such conversion processes is required to ensure authenticity and trust. This process of quality assurance is a key obstacle to achieving scalability for large volumes of content. In this article, we address the quality assurance process for the preservation of born-digital photographs and validate conversions of raw image formats into standard formats such as Adobe Digital Negative. To achieve this, we rely on a systematic planning framework. We classify requirements that have to be evaluated according to their measurement needs. We extend an existing measurement framework using a combination of tools, image similarity algorithms, and purpose-built plugins. By combining metadata extraction, image rendering and comparison, and perceptual-level quality assurance, we evaluate the feasibility of automating the core part of quality assurance that is often the most costly part of preservation processes.