Real-time image-processing differs from 'ordinary' image-processing in that the logical correctness of the system requires both correct and timely outputs. And, although special real-time imaging architectures are available or can theoretically be constructed, there are no standard programming languages to support these architectures. Moreover, many applications cannot or do not take advantage of any special imaging architectures; they use standard processors instead. In this case, optimization of run-time code is necessarily done with hand-tuning or by trial-and-error. In this paper these optimization techniques are formalized and it is shown how they can be applied to imaging algorithms in real-time to improve run-time performance.
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