MRF-based true motion estimation using H.264 decoding information

Markov Random Field (MRF) has been successfully used to formulate the energy minimization problems in computer vision. However, a multi-label MRF model such as the conventional true motion estimation approach requires a significant amount of computation due to its large search space. Besides, we observe that decoding information obtained from H.264/AVC could be applied to reduce the computational complexity of true motion estimation. In this paper, a new true motion estimation scheme is proposed. We analyze the motion information and macroblock types from H.264/AVC decoder. According to the decoding information, predictors from the obtained motion vectors (MVs) are selected for MRF models. With these predictors, the search space of MRF could be reduced from O(n
²
) to O(n) compared to conventional full search scheme. Experimental results evaluated on the Middlebury optical flow benchmarks show that our proposed scheme is able to optimize the MV field of H.264/AVC decoder to approximate the true motion field.