The two most prominent features of early visual processing are orientation selective filtering and contrast gain control. While the effect of orientation selectivity can be assessed within in a linear model, contrast gain control is an inherently nonlinear computation. Here we employ the class of L_p elliptically contoured distributions to investigate the extent to which the two features, orientation selectivity and contrast gain control, are suited to model the statistics of natural images. Within this model we find that contrast gain control can play a significant role for the removal of redundancies in natural images.