Non-linear Invertible Representation for Joint Statistical and Perceptual Feature Decorrelation

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 analyse 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 stored. 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.