  This paper introduces a framework for distributed parallel image signal
extrapolation. Since high-quality image signal processing often comes along
with a high computational complexity, a parallel execution is desirable. The
proposed framework allows for the application of existing image signal
extrapolation algorithms without the need to modify them for a parallel
processing. The unaltered application of existing algorithms is achieved by
dividing input images into overlapping tiles which are distributed to compute
nodes via Message Passing Interface. In order to keep the computational
overhead low, a novel image tiling algorithm is proposed. Using this algorithm,
a nearly optimum tiling is possible at a very small processing time. For
showing the efficacy of the framework, it is used for parallelizing a
high-complexity extrapolation algorithm. Simulation results show that the
proposed framework has no negative impact on extrapolation quality while at the
same time offering good scaling behavior on compute clusters.
