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Statistical based motion estimation for video coding

In this work, statistical based motion estimation is applied to the problem of motion estimation for video coding. We show that the motion equations of a rigid body can be formulated as a nonlinear dynamic system whose state is represented by the motion parameters and by the scaled depths of the object feature points. An extended Kalman filter is used to estimate the global motion, from which successive frames can be predicted in a motion compensated video coding system. The structure imposed by the model implies that the reconstructed motion is very natural in comparison to more common block-based schemes. Moreover, the parametrization of the model allows for a very efficient coding of motion information.