Quantitative Quality Assessment of Computational Photography (A06)

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Computational Photography (CP) opens up novel possibilities for digital imaging, but the diversity of the approaches described in the literature pose problems regarding the objective assessment of their respective performance. This project proposes to address this by a number of steps: (1) identifying a set of benchmark scenes which can be inexpensively and precisely reproduced with quantified uncertainty to serve as litmus tests for new methods; (2) defining, acquiring and distributing comprehensive light field data sets to be used as benchmarks in realistic simulations (3) develop predictive performance metrics for CP as evaluated on salient regions of the benchmark scenes.