Document image quality: making fine discriminations

We estimate, using synthetically generated images, the smallest changes in document image quality that can be distinguished reliably and fully automatically by T. Kanungo´s (1996) bootstrapping test. Six parameters of a physics based document image degradation model (H.S. Baird, 1992), are varied, one at time: for each, over a range of parameter value differences, two sets of synthetic images are generated pseudorandomly and the two sets tested for statistical equivalence using Kanungo´s method. The rate at which Kanungo´s method rejects the hypothesis that the two sets are drawn from the same distribution is analyzed as a function of parameter difference (a specialized “power function”). The finest discriminations afforded by the method are given by the width of the power function at a low fixed reject threshold. The data show that remarkably fine discriminations are possible-often subtler than are evident to visual inspection-for all six parameters. As few as 25 reference images are sufficient. These results suggest that Kanungo´s method is sufficiently sensitive to a wide range of physics based image degradations to serve as an engineering foundation for many image quality estimation and OCR engineering purposes