NDLI: Structural pattern-based approach for Betacam dropout detection in degraded archived media

Content Provider	IET Digital Library
Author	Song, Gihun Roy, Kaushik Ahn, Kiok Wadud, M. Abdullah Al Iqbal, Md. Tauhid Bin Chae, Oksam
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.
Starting Page	224
Ending Page	232
Page Count	9
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5859
Journal	IET Image Processing
Publisher Date	2018-10-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Method Betacam Dropout Defects Betacam Dropout Detection Method Betacam Dropout Error Detection Degraded Archived Media Degraded Data Digitisation Process Information Retrieval System Information Storage And Retrieval Multiple Consecutive Frames Office Automation Old Archived Media Records Management Single Frame Structural Pattern-based Approach Successive Frames Temporal Information-based Detector Vertical Pattern
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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