Structural pattern‐based approach for Betacam dropout detection in degraded archived media. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Song, Gihun; Roy, Kaushik; Ahn, Kiok; Wadud, M. Abdullah‐Al‐; Iqbal, Md. Tauhid Bin; Chae, Oksam
• Is Part Of: IET image processing. Volume 13:Issue 1(2019)
• Page Start: 224
• Page End: 232
• Publication Date: 2019-01-01
• Subjects: information retrieval systems -- records management; degraded archived media -- Betacam dropout defects -- digitisation process -- old archived media -- degraded data -- multiple consecutive frames -- successive frames -- automatic method -- Betacam dropout error detection -- vertical patterns -- single frame -- temporal information‐based detectors -- Betacam dropout detection method -- structural pattern‐based approach; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5859
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Detection of Betacam dropout defects that can occur in the digitisation process of old archived media has importance in the restoration of degraded data to a higher quality. Most of the existing methods rely on the temporal information of multiple consecutive frames to detect Betacam dropouts, which sometimes may not work well as several successive frames may contain a Betacam error at the same position. In this study, an automatic method of Betacam dropout error detection is proposed based on vertical patterns in a single frame. Hence, it is also applicable when temporal information‐based detectors fail. The results of performance tests done in real working environments demonstrate that the proposed Betacam dropout detection method performs much better than the existing methods.