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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.