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An adaptive deinterlacing algorithm based on motion compensation is presented. It consists of the detection of motion blocks, the adaptive motion estimation with Kalman filtering, and the motion compensation for motion blocks and field repetition for static blocks. The detection of motion blocks can accurately identify the motion blocks by using successive 4field images. The motion estimation module with Kalman filtering searches motion vectors only for motion blocks, and the search model is adaptive to motion velocity and acceleration. Two deinterlacing methods are adopted to satisfy the different requirements of motion blocks and static blocks. Compared with full search algorithm, the proposed algorithm greatly reduces the computational amount while keeping the performance approximately.
An adaptive deinterlacing algorithm based on motion compensation is presented. It consists of the detection of motion blocks, the adaptive motion estimation with Kalman filtering, and the motion compensation for motion blocks and field repetition for static blocks. The detection of motion blocks can accurately identify the motion blocks by using successive 4field images. The motion estimation module with Kalman filtering searches motion vectors only for motion blocks, and the search model is adaptive to motion velocity and acceleration. Two deinterlacing methods are adopted to satisfy the different requirements of motion blocks and Compared with full search algorithm, the proposed algorithm greatly reduces the computational amount while keeping the performance approximately.