Classification based data mixing for hybrid de-interlacing techniques

De-interlacing is one of the key technologies in modern displays and multimedia personal computers. Various methods have been proposed including motion compensated (MC) methods and non motion compensated methods. Hybrid methods that combine different de-interlacing techniques are widely used to take...

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Bibliographic Details
Published in13th European Signal Processing Conference (EUSIPCO 2005) pp. 1 - 4
Main Authors Zhao, M., Ciuhu, C., de Haan, G.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.09.2005
Subjects
Algorithms
Bicycles
Classification
Criteria
Displays
Equations
Interpolation
Least squares approximations
Mathematical model
Multimedia
Optimization
Personal computers
Pixels
Training
Video sequences
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Summary:De-interlacing is one of the key technologies in modern displays and multimedia personal computers. Various methods have been proposed including motion compensated (MC) methods and non motion compensated methods. Hybrid methods that combine different de-interlacing techniques are widely used to take advantages from individual algorithms. The combination is normally based on the quality criterion of individual de-interlacing algorithms. In this paper, we propose a classification based data mixing algorithm for hybrid de-interlacing. The algorithm first classifies the interpolated pixels from individual de-interlacing methods and then mix them to give the final output. The optimal mixing coefficients are obtained from an off-line training, which employs the Least Mean Squared (LMS) algorithm.
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ISBN:1604238216
9781604238211