Fjj Frans Blommaert

Color reproduction and the naturalness constraint

This article aims to determine the process underlying the subjective impression about the fidelity of reproduced object colors. To this end, we present the concept of the naturalness constraint and a framework for specification of naturalness judgments. We consider several research questions that are essential for this framework and discuss plausible answers supported by experiments. In general, naturalness assessment of reproduced object colors can be (1) defined as similarity to prototypical object colors, and (2) characterized by a probability density function (e.g., Gaussian). Experiments show that (3) there is a considerable amount of consistency in naturalness judgments of locally and globally processed images (although observers are slightly more tolerant of global image processing), and (4) naturalness judgments vary for different object categories; e.g., subjects are more consistent in naturalness judgments of skin, grass, and sky reproductions than shirt reproduction. We suggest that (5) naturalness of a whole picture is determined by the naturalness of the most critical object in that picture. Finally, we introduce a naturalness index predicting perceived naturalness of color reproduction of real-life scenes.

Instabilities in a continuous medium model for the retina

The shape of the spatial response of the retina on a small light stimulus as found by Rodieck (1965) for the cat, and proposed for the human retina among others by Korn and von Seelen (1972), resembles a Mexican hat known as a sombrero. A model presented by Röhler (1976), using a continuous medium as a description of the retina, can lead to such a “Mexican hat” response function for a specific choice of parameters. The spatial Fourier transform of this response function has a general appearance that corresponds to that calculated for stationary signals. However, such an analysis of the model for stationary signals is incomplete. Inspection of the time-dependent equations shows that it is unstable precisely for those parameter values that give the stationary response function its desired shape. Such stationary situations cannot be physically realized since the model is unstable.