Nonuniform image motion estimation in reduced coefficient transformed domains

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Abstract
The transformed domain maximum likelihood (TDML) algorithm for image motion estimation is presented. This algorithm finds a solution which maximizes a log-likelihood function using a steepest ascent scheme. Important characteristics of the algorithm are the inclusion of noise in the signal model, the consideration of motion as a nonuniform process, and calculation of convergence parameters by means of a linear analysis. Simulation on real image sequences demonstrate the validity of the motion estimator. The experiments also verify the validity of the equations presented for the calculation of the convergence parameters. Additional experiments performed to determine the noise sensitivity of the TDML show that noise resistance can be obtained using a reduced coefficient transform (RCT) TDML algorithm. An additional benefit of using an RCT with the TDML algorithm is an increase in the speed of the algorithm without significant performance degradation. Two of the common transforms, Haar and Walsh-Hadamard, are shown to have some interesting properties when utilized with the RCT-TDML algorithm

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