Normalized image representation for efficient coding
In this paper we propose an adaptive nonlinear image representation based on the divisive normalization of local-frequency transforms used in contrast masking models. This normalized representation has two effects: (1) it increases the statistical independence of the coefficients of the representation and (2) it is Euclidean from a perceptual point of view. Experimental results show that reducing the remaining statistical and perceptual dependence using normalized representations for transform coding may make a big difference in the qualify of the reconstructed images.