Javier Portilla

Eduardo Martinez-Enriquez, Maria del Mar Gonzalez, Javier Portilla

We introduce a method for deterministic decoupling of global features and show its applicability to improve data analysis performance, as well as to open new venues for feature transfer. We propose a new formalism that is based on defining transformations on submanifolds, by following trajectories along the features gradients. Through these transformations we define a normalization that, we demonstrate, allows for decoupling differentiable features. By applying this to sampling moments, we obtain a quasi-analytic solution for the orthokurtosis, a normalized version of the kurtosis that is not just decoupled from mean and variance, but also from skewness. We apply this method in the original data domain and at the output of a filter bank to regression and classification problems based on global descriptors, obtaining a consistent and significant improvement in performance as compared to using classical (non-decoupled) descriptors.

Alexander Hepburn, Raul Santos-Rodriguez, Javier Portilla

The Two Alternative Forced Choice (2AFC) paradigm offers advantages over the Mean Opinion Score (MOS) paradigm in psychophysics (PF), such as simplicity and robustness. However, when evaluating perceptual distance models, MOS enables direct correlation between model predictions and PF data. In contrast, 2AFC only allows pairwise comparisons to be converted into a quality ranking similar to MOS when comparisons include shared images. In large datasets, like BAPPS, where image patches and distortions are combined randomly, deriving rankings from 2AFC PF data becomes infeasible, as distorted images included in each comparisons are independent. To address this, instead of relying on MOS correlation, researchers have trained ad-hoc neural networks to reproduce 2AFC PF data based on pairs of model distances - a black-box approach with conceptual and operational limitations. This paper introduces a more robust distance-model evaluation method using a pure probabilistic approach, applying maximum likelihood estimation to a binomial decision model. Our method demonstrates superior simplicity, interpretability, flexibility, and computational efficiency, as shown through evaluations of various visual distance models on two 2AFC PF datasets.