Motion Adaptive Deinterlacing With Modular Neural Networks

Authors:
Hyunsoo Choi; Chulhee Lee
Affiliations:
Dept. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea;-
Venue:
IEEE Transactions on Circuits and Systems for Video Technology
Year:
2011

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Abstract

In this letter, a motion adaptive deinterlacing algorithm based on modular neural networks is proposed. The proposed method uses different neural networks based on the amount of motion. Modular neural networks were selectively used depending on the differences between the adjacent fields. We also used motion vectors to select optimal input pixels from the adjacent fields. Motion estimation was used to find input blocks for the neural networks with minimum errors. Intra/inter-mode switching was employed to address inaccurate motion estimation problems.