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http://dx.doi.org/10.5573/ieie.2014.51.7.190

DCT-based Digital Dropout Detection using SVM  

Song, Gihun (Department of Computer Engineering, KyungHee University)
Ryu, Byungyong (Department of Computer Engineering, KyungHee University)
Kim, Jaemyun (Department of Computer Engineering, KyungHee University)
Ahn, Kiok (Department of Computer Engineering, KyungHee University)
Chae, Oksam (Department of Computer Engineering, KyungHee University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.7, 2014 , pp. 190-200 More about this Journal
Abstract
The video-based system of the broadcasters and the video-related institutions have shifted from analogical to digital in worldwide. This migration process can generate a defect, digital dropout, in the quality of the contents. Moreover, there are limited researches focused on these kind of defects and those related have limitations. For that reason, we are proposing a new method for feature extraction emphasizing in the peculiar block pattern of digital dropout based on discrete cosine transform (DCT). For classification of error block, we utilize support vector machine (SVM) which can manage feature vectors efficiently. Further, the proposed method overcome the limitation of the previous one using continuity of frame by frame. It is using only the information of a single frame and works better even in the presence of fast moving objects, without the necessity of specific model or parameter estimation. Therefore, this approach is capable of detecting digital dropout only with minimal complexity.
Keywords
Digital Dropout; Digital Archive; Discrete Cosine Transform; Support Vector Machine;
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