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Proceedings Paper

A trained filter de-interlacer based on complex classification
Author(s): Dmitry Znamenskiy; Marco Kruse
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Paper Abstract

A novel trained filter based scheme for video de-interlacing is proposed and described in detail. This scheme uses different classifiers, called error functions, on the input, and mixes several sub-de-interlacers depending on them. The approach differs from the earlier works in this area due to focus on more complex classification rather than on complex sub-de-interlacers. The proposed scheme is flexible and allows various combinations of error functions with sub-de-interlacers. In this article we describe a test implementation of this concept with five different sub-de-interlacers and five error functions composing a spatial-temporal de-interlacing method. The description of the test implementation is supported by simulations where we evaluate the contribution of different sub-de-interlacers and error function to output de-interlacing quality.

Paper Details

Date Published: 19 January 2009
PDF: 9 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72571C (19 January 2009); doi: 10.1117/12.805906
Show Author Affiliations
Dmitry Znamenskiy, Philips Research Labs. (Netherlands)
Marco Kruse, Univ. Karlsruhe (Germany)


Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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