Transformations and processing operations are critical to MDP. Transformations such as scaling, reverse tone and orientation, along with processing including sizing, Boolean operations and data filtering are key parts of this. These techniques are often employed in sequence and/or in parallel in a complex functional chain. While transformations typically are done "up front" when the data is input, processing is less straightforward, involving multiple reads and writes to handle the more intricate functionality and also the collection of input patterns which may be required to produce the data that comprises a single mask.
The approach detailed in this paper consists of two complementary techniques: efficient MDP flow and jobdeck mapping. Efficient MDP flow is achieved by pipelining the output of each step to the input of the subsequent step. Rather than writing the output of a particular processing step to file and then reading it in to the following step, the pipelining or chaining of the steps results in an efficient flow with minimal file I/O.
The efficient MDP flow is enhanced by a technique called jobdeck mapping which allows in essence an unlimited number of pattern inputs by taking each transformed pattern and including it in an input jobdeck. Making use of established jobdeck handling capabilities, the user-selected input pattern/transformation combinations are mapped to an input jobdeck which is processed by the advanced flow, allowing great flexibility and user control of the process.