Preferred image quality metric for shifted superimposition-based resolution-enhanced images

Abstract

Shifted superimposition is a resolution-enhancement method that has gained popularity in the projector industry the last couple of years. This method consists of shifting every other projected frame spatially with subpixel precision, and by doing so creating a new pixel grid on the projected surface with smaller effective pixel pitch. There is still an open question of how well this technique performs in comparison with the native resolution, and how high the effective resolution gain really is. To help investigate these questions, we have developed a framework for simulating different superimposition methods over different image contents, and evaluate the result using several image quality metrics (IQMs). We have also performed a subjective experiment with observers who rate the simulated image content, and calculated the correlation between the subjective results and the IQMs. We found that the visual information fidelity metric is the most suitable to evaluate natural superimposed images when subjective match is desired. However, this metric does not detect the distortion in synthetic images. The multiscale structural similarity metric which is based on the analysis of image structure is better at detecting this distortion.