Birefringent media, like biological tissues, are usually assumed to be uniaxial. For biological tissues, the influence of linear birefringence on the scattering phase function is assumed to be neglectable. In order to examine this, a numerical study of the influence of linear birefringence on the scattering phase function and the resulting backscattering Mueller matrices was performed. It is assumed that the media consist of spherical scattering particles embedded in a nonabsorbing medium, which allows us to employ the Lorenz-Mie theory. In the Monte Carlo framework, the influence of linear birefringence on the components of the electric field vector is captured through the Jones N-matrix formalism. The Lorenz-Mie theory indicates that a given linear birefringence value n has a bigger impact on the scattering phase function for large particles. This conclusion is further supported by Monte Carlo simulations, where the phase function was calculated based on the refractive index once in the ordinary direction and once in the extraordinary one. For large particles, comparisons of the resulting backscattering Mueller matrices show significant differences even for small n values.
The measurement of image quality requires the judgement by the human visual system. This paper describes
a psycho-visual test technique that uses the internet as a test platform to identify image quality in a more
time-effective manner, comparing the visual response data with the results from the same test in a lab-based
environment and estimate the usefulness of the internet as a platform for scaling studies.
This work presents a model for dotgain prediction using repetitive patterns based on the characterization of
neighboring and clustering effects of a specific printing device. Estimating dotgain is done nowadays by measuring
patches of color patterns realized by a specific printing device. Current models use the information about adjacent
dots to predict dotgain. However, research has shown that dotgain is influenced by the neighborhood of a dot
which in general is bigger than one dot-size, in particular in connection with laser printers. The presented method
predicts the dotgain of a dot considering a larger surrounding based on the observation of two main parameters
affecting the luminance of a pattern which can be fitted using linear regression.