An effective de-interlacing technique using motion compensated interpolation
IEEE Transactions on Consumer Electronics
Direction-oriented interpolation and its application to de-interlacing
IEEE Transactions on Consumer Electronics
Deinterlacing using directional interpolation and motion compensation
IEEE Transactions on Consumer Electronics
An effective de-interlacing technique using two types of motion information
IEEE Transactions on Consumer Electronics
Motion adaptive interpolation with horizontal motion detection for deinterlacing
IEEE Transactions on Consumer Electronics
New edge dependent deinterlacing algorithm based on horizontal edge pattern
IEEE Transactions on Consumer Electronics
Efficient de-interlacing technique by inter-field information
IEEE Transactions on Consumer Electronics
An efficient and robust adaptive deinterlacing technique
IEEE Transactions on Consumer Electronics
New edge-directed interpolation
IEEE Transactions on Image Processing
Motion compensation assisted motion adaptive interlaced-to-progressive conversion
IEEE Transactions on Circuits and Systems for Video Technology
Hybrid de-interlacing algorithm based on motion vector reliability
IEEE Transactions on Circuits and Systems for Video Technology
Video de-interlacing by adaptive 4-field global/local motion compensated approach
IEEE Transactions on Circuits and Systems for Video Technology
An adaptive motion-compensated approach for video deinterlacing
Multimedia Tools and Applications
Journal of Visual Communication and Image Representation
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De-interlacing based on motion compensation (MC) is one of the best ways of improving the resolution of a progressive video converted from an interlaced source. However, the converted frames often suffer from serious defects like feathering artifacts in regions with inaccurate motion vectors (MVs). In such regions, an intra-field method that is robust to MV errors can be used to correct motion compensation artifacts (MCAs). In this letter, we propose an adaptive arbitration method to combine intra-field and MC methods adequately. The proposed method considers the reliability of MC results along with the MV reliability measured by the spatio-temporal consistency of MVs and displaced pixel differences. The MC reliability is determined by detecting MCAs in MC results, and then the MV reliability is adjusted according to the MC reliability. Also, adaptive-weight MC and pseudo MC methods are proposed to provide more reliable MC results and to improve the accuracy of MCA detection, respectively. Experimental results show that the proposed method provides high-quality video sequences while reducing many visible artifacts.