Motion Vector Context-based Adaptive 3-D Recursive Search Block Matching Motion Estimation
نویسندگان
چکیده
In this paper, based on the analysis to relationship between the currently being processed block and its neighbors, a motion vector context-based adaptive 3-D recursive search (MVCA-3DRS) block matching motion estimation algorithm is developed. In the proposed algorithm, the candidate vectors are partitioned into two categories, one stationary vector implying the current block belongs to one of neighboring large objects, one nonstationary vector implying the current block either belongs to one of neighboring small objects, or is a new object, then predicted with extended vector median estimator and anti-median estimator as well as random-updated estimator based on the spatial-temporal motion vector contexts. The simulation results show that the proposed algorithm can significantly improve the consistence of the resulting motion vector field. Test experiments on motion-compensation (MC) deinterlaced system with typical video sequences confirm that, compared with the 3DRS algorithm, the proposed MVCA-3DRS can significantly improve the interpolated images quality. Although introducing the additional vector filtering operations, the proposed algorithm still can maintain a comparative computation complexity with that of the 3DRS algorithm due to its shrink to the number of candidate vectors. Key-Words: 3DRS, block matching, motion estimation, de-interlace, frame rate up-conversion
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