| Summary: | 碩士 === 義守大學 === 資訊工程學系 === 91 === Motion estimation and compensation is widely used in video coding systems. To find motion vectors (MV) that lead to high compression, most conventional motion estimation approaches use a source distortion measure, such as mean-square error (MSE) or mean absolute error (MAE), as a search criterion. The resulting MV is used to generate a motion compensated prediction block and the motion compensated prediction difference frame (called residue blocks). When used in low bit-rate or very-low bit-rate video coding system, the bit rate for motion vector is more important than for residue in total bit-rate. Thus a joint rate and distortion (R-D) optimal motion estimation has been developed to achieve the trade-off between MV coding and residue coding.
We present a new algorithm using Kalman filter to enhance the performance of the conventional R-D motion estimation at a relatively low computation cost. We try to amend the incorrect and/or inaccurate estimate of motion with higher precision by using Kalman filter.
We first obtain a measurement of motion vector of a block by using an existing R-D motion estimation scheme. Then generate the predicted motion vector utilizing the inter-block correlation in neighboring blocks. Based on these two motion information, a simple one-dimension motion model is developed and then Kalman filter applied to obtain the optimal estimate of motion vector.
Simulation results show that the proposed technique can efficiently improve the R-D motion estimation performance.
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