Putting the visual system noise back in the picture.

Computable expressions for the input-picture-equivalent contrast noise of the visual system are provided for the locally linear subclass of nonlinear models, where the internal model noise is allowed to be signal dependent. The equivalent-noise concept is thereby extended to many of the models developed to explain masking and discrimination among suprathreshold stimuli. For these models the equivalent noise depends on the masking stimulus, and its structure can be strongly determined by the representation of the masker at the level of the system at which the performance-limiting noise is generated. The expressions are applicable to the case of less-than-full-rank transformations. Pictures that have hypothetical visual-system noise projected back into them can provide insights into efficient picture-coding algorithms.