  Steered-Mixtures-of Experts (SMoE) present a unified framework for sparse
representation and compression of image data with arbitrary dimensionality.
Recent work has shown great improvements in the performance of such models for
image and light-field representation. However, for the case of videos the
straight-forward application yields limited success as the SMoE framework leads
to a piece-wise linear representation of the underlying imagery which is
disrupted by nonlinear motion. We incorporate a global motion model into the
SMoE framework which allows for higher temporal steering of the kernels. This
drastically increases its capabilities to exploit correlations between adjacent
frames by only adding 2 to 8 motion parameters per frame to the model but
decreasing the required amount of kernels on average by 54.25%, respectively,
while maintaining the same reconstruction quality yielding higher compression
gains.
