Valero Laparra

TL;DR: In this article, a nonlinear analysis transformation, a uniform quantizer, and a non-linear synthesis transformation are used to optimize the entire model for rate-distortion performance over a database of training images.

TL;DR: In this paper, a nonlinear analysis transformation, a uniform quantizer, and a non-linear synthesis transformation are used to optimize the entire model for rate-distortion performance over a database of training images.

TL;DR: In this paper, the main theoretical Gaussian Process (GPs) developments in the field of biogeophysical parameter retrieval are reviewed, considering new algorithms that respect signal and noise characteristics, extract knowledge via automatic relevance kernels, and allow applicability of associated uncertainty intervals to transport GP models in space and time that can be used to uncover causal relations between variables and can encode physically meaningful prior knowledge via radiative transfer model (RTM) emulation.

TL;DR: In this paper, a parametric nonlinear transformation is proposed for Gaussianizing data from natural images, where each component is normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and an additive constant.

TL;DR: In this article, the authors introduce a general framework for end-to-end optimization of the rate-distortion performance of nonlinear transform codes assuming scalar quantization, which can be used to optimize any differentiable pair of analysis and synthesis transforms in combination with any perceptual metric.