| Abstract
The aim of many image mappings is representing
the signal in a basis of decorrelated features. Two fundamental aspects
must be taken into account in the basis selection problem: data distribution
and the qualitative meaning of the underlying space. The classical PCA
techniques reduce the statistical correlation using the data distribution.
However, in applications where human vision has to be taken into account,
there are perceptual factors that make the feature space uneven, and additional
interaction among the dimensions may arise.
In this work a common framework is presented
to analyze the perceptual and statistical interactions among the coefficients
of any representation. Using a recent non-linear perception model a set
of input-dependent features is obtained which simultaneously remove the
statistical and perceptual correlations between coefficients. A fast method
to invert this representation is also presented, so no input-dependent
transform has to be storaged. The decorrelating power of the proposed representation
suggests that it is a promising alternative to the linear transforms used
in image coding, fusion or retrieval applications.
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