14.01.2014

New abstract by David Janssen and Felix Wichmann

David and Felix's abstract was acceppted as a poster at TeaP in Giessen from 30.03.-02.04.2014. Title: "Subband decompositions are inherently incompatible with (most) non-linear models of visual perception"

Abstract:

"Subband decompositions are inherently incompatible with (most) non-linear models of visual perception"

Image-driven models in vision predict image perceptibility. Typically, such models combine subband decomposition (e.g. Steerable Pyramid) with a late, non-linear decision stage (e.g. Minkowski norm).

The interaction between subband decompositions and the decision non-linearity, however, creates a problem. A subband decomposition represents images as a set of overlapping bands with different peak sensitivities in frequency and orientation. Image content that matches a peak sensitivity is represented as a maximal value in its band. Frequencies or orientations that fall between two peak sensitivities, however, are represented as multiple smaller values in adjacent bands.

For example, the Minkowksi norm---a standard decision non-linearity---maps the "activity" values within the bands to a single value. Depending on the Minkowski-exponent beta, the norm weighs the values in the vector equally or increasingly favours the highest values. Thus, the contribution of image content falling close to the peak sensitivities of the bands is treated differently to that falling between peaks. As a result, models combining subband decomposition with non-linear processing show an undesirable dependence of their response on the---arbitrarily chosen---peak sensitivities.

Such a dependence implies, for example, that image detectability should oscilate with viewing distance. This is clearly not observed in reality.