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A trained filter de-interlacer based on complex classification

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.