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A low-complexity vector propagation (VP) algorithm is introduced for the estimation of bidirectional motion vector fields in image sequences. The proposed VP algorithm exploits the strong correlation between forward and backward motion vector fields in image sequences. The performance of the VP algorithm is compared to that of a bidirectional multiresolution blockmatching (MRBM) motion estimation (ME) algorithm. Computer simulation results demonstrate that with the VP algorithm, the computational workload of the bidirectional ME is reduced by a factor of nearly 2. The robustness of the VP algorithm is extensively tested using computer generated image sequences and real movies for a motion picture restoration (MPR) system. It is shown that the VP algorithm is robust enough to be used in the computationally demanding MPR algorithm, in which the performance of the novel VP algorithm is close to that of a bidirectional MRBM ME algorithm. With this technique, other video processing systems that desire to take advantage of bidirectional motion estimation can now do so without an excessive increase in computing time.

Abstract-Gibbs-Markov random field (GMRF) modeling has been shown to be a robust method in the detection of missing-data in image sequences for a video restoration application. However, the maximum a posteriori probability (MAP) estimation of the GMRF model requires computationally expensive optimization algorithms in order to achieve an optimal solution. The continuous relaxation labeling (RL) is explored in this paper as an efficient approach for solving the optimization problem. The conversion of the original combinatorial optimization into a continuous RL formulation is presented. The performance of the RL formulation is analyzed and compared with that of other optimization methods such as stochastic simulated annealing, iterated conditional modes, and mean field annealing. The results show that RL holds out promise as an optimization algorithm for problems in image sequence processing.

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