Motion compensation de interlacing is expected to be better than linear techniques; but all the block based motion compensation de interlacing methods cause block artifacts. The algorithm proposed in this paper is con...Motion compensation de interlacing is expected to be better than linear techniques; but all the block based motion compensation de interlacing methods cause block artifacts. The algorithm proposed in this paper is concerned with reducing the deficiency of motion compensated interpolation by using adaptive hybrid de interlacing methods. A spatio temporal tensor based approach is used to get more accurate motion field for de interlacing. Motion vector is assigned for each position with pixel precision; the block artifact is reduced significantly. To deal with the artifacts introduced by motion compensation when the motion estimation is incorrect, linear techniques are considered by adaptive weighting. Furthermore, directional filter is adapted to preserve details and the edge discontinuity could be eliminated greatly. Our approach is robust to incorrect motion vector estimation.展开更多
An adaptive de-interlacing 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 moti...An adaptive de-interlacing 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 4-field 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 de-interlacing 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.展开更多
This paper introduces a new method of converting interlaced video to a progressively scanned video and image, The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the M...This paper introduces a new method of converting interlaced video to a progressively scanned video and image, The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the MAP is done by minimizing the energy functional, The half-quadratic regularization method is used to solve the corresponding partial differential equations （PDEs）, This approach gives the improved results over the conventional de-interlacing methods, Two criteria are proposed in the paper, and they can be used to evaluate the performance of the de-interlacing algorithms,展开更多
文摘Motion compensation de interlacing is expected to be better than linear techniques; but all the block based motion compensation de interlacing methods cause block artifacts. The algorithm proposed in this paper is concerned with reducing the deficiency of motion compensated interpolation by using adaptive hybrid de interlacing methods. A spatio temporal tensor based approach is used to get more accurate motion field for de interlacing. Motion vector is assigned for each position with pixel precision; the block artifact is reduced significantly. To deal with the artifacts introduced by motion compensation when the motion estimation is incorrect, linear techniques are considered by adaptive weighting. Furthermore, directional filter is adapted to preserve details and the edge discontinuity could be eliminated greatly. Our approach is robust to incorrect motion vector estimation.
文摘An adaptive de-interlacing 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 4-field 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 de-interlacing 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.
文摘This paper introduces a new method of converting interlaced video to a progressively scanned video and image, The new method is derived from Bayesian framework with the spatial-temporal smoothness constraint and the MAP is done by minimizing the energy functional, The half-quadratic regularization method is used to solve the corresponding partial differential equations （PDEs）, This approach gives the improved results over the conventional de-interlacing methods, Two criteria are proposed in the paper, and they can be used to evaluate the performance of the de-interlacing algorithms,
