120 citations · 353 across the 35 of their papers we have counts for
37 papers
On the RAID dataset of perceptual responses: analysis and statistical causes
Paula Daudén-Oliver, David Agost-Beltran, Emilio Sansano-Sansano +4
This work analyzes the RAID dataset to evaluate human responses to affine image distortions, including rotation, translation, scaling, and Gaussian noise. Using Mean Squared Error…
RAID-Database: human Responses to Affine Image Distortions
Paula Daudén-Oliver, David Agost-Beltran, Emilio Sansano-Sansano +3
Image quality databases are used to train models for predicting subjective human perception. However, most existing databases focus on distortions commonly found in digital media a…
Parametric PerceptNet: A bio-inspired deep-net trained for Image Quality Assessment
Jorge Vila-Tomás, Pablo Hernández-Cámara, Valero Laparra +1
Human vision models are at the core of image processing. For instance, classical approaches to the problem of image quality are based on models that include knowledge about human v…
The Effect of Perceptual Metrics on Music Representation Learning for Genre Classification
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
The subjective quality of natural signals can be approximated with objective perceptual metrics. Designed to approximate the perceptual behaviour of human observers, perceptual met…
Assessing invariance to affine transformations in image quality metrics
Nuria Alabau-Bosque, Paula Daudén-Oliver, Jorge Vila-Tomás +2
Subjective image quality metrics are usually evaluated according to the correlation with human opinion in databases with distortions that may appear in digital media. However, thes…
Image Segmentation via Divisive Normalization: dealing with environmental diversity
Pablo Hernández-Cámara, Jorge Vila-Tomás, Paula Dauden-Oliver +3
Autonomous driving is a challenging scenario for image segmentation due to the presence of uncontrolled environmental conditions and the eventually catastrophic consequences of fai…
Data is Overrated: Perceptual Metrics Can Lead Learning in the Absence of Training Data
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
Perceptual metrics are traditionally used to evaluate the quality of natural signals, such as images and audio. They are designed to mimic the perceptual behaviour of human observe…
What You Hear Is What You See: Audio Quality Metrics From Image Quality Metrics
Tashi Namgyal, Alexander Hepburn, Raul Santos-Rodriguez +2
In this study, we investigate the feasibility of utilizing state-of-the-art image perceptual metrics for evaluating audio signals by representing them as spectrograms. The encourag…
Psychophysics of Artificial Neural Networks Questions Classical Hue Cancellation Experiments
Jorge Vila-Tomás, Pablo Hernández-Cámara, Jesús Malo
We show that classical hue cancellation experiments lead to human-like opponent curves even if the task is done by trivial (identity) artificial networks. Specifically, human-like…
Image Statistics Predict the Sensitivity of Perceptual Quality Metrics
Alexander Hepburn, Valero Laparra, Raúl Santos-Rodriguez +1
Previously, Barlow and Attneave hypothesised a link between biological vision and information maximisation. Following Shannon, information was defined using the probability of natu…
Analysis of Deep Image Quality Models
Pablo Hernández-Cámara, Jorge Vila-Tomás, Valero Laparra +1
Subjective image quality measures based on deep neural networks are very related to models of visual neuroscience. This connection benefits engineering but, more interestingly, the…
Functional Connectome of the Human Brain with Total Correlation
Qiang Li, Greg Ver Steeg, Shujian Yu +1
Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such a…
Functional Connectivity via Total Correlation: Analytical results in Visual Areas
Qiang Li, Greg Ver Steeg, Jesus Malo
Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those…
Orthonormal Convolutions for the Rotation Based Iterative Gaussianization
Valero Laparra, Alexander Hepburn, J. Emmanuel Johnson +1
In this paper we elaborate an extension of rotation-based iterative Gaussianization, RBIG, which makes image Gaussianization possible. Although RBIG has been successfully applied t…
Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset
Pablo Hernández-Cámara, Valero Laparra, Jesús Malo
One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensor…
Paraphrasing Magritte's Observation
Jesus Malo
Contrast Sensitivity of the human visual system can be explained from certain low-level vision tasks (like retinal noise and optical blur removal), but not from others (like chroma…
On the relation between statistical learning and perceptual distances
Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez +2
It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics o…
Contrast Sensitivity Functions in Autoencoders
Qiang Li, Alex Gomez-Villa, Marcelo Bertalmio +1
Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reasses…
Information Theory in Density Destructors
J. Emmanuel Johnson, Valero Laparra, Gustau Camps-Valls +2
Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multiva…
Estimating the contribution of early and late noise in vision from psychophysical data
Jesus Malo, Jose Juan Esteve-Taboada, Guillermo Aguilar +2
In many psychophysical detection and discrimination tasks human performance is thought to be limited by internal or inner noise when neuronal activity is converted into an overt be…
Information Theory Measures via Multidimensional Gaussianization
Valero Laparra, J. Emmanuel Johnson, Gustau Camps-Valls +2
Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems. It has several desirable properties for real world applications…
Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications
A. Gomez-Villa, A. Martín, J. Vazquez-Corral +2
Visual illusions allow researchers to devise and test new models of visual perception. Here we show that artificial neural networks trained for basic visual tasks in natural images…
Information Flow in Biological Networks for Color Vision
Jesus Malo
Color Appearance Models are biological networks that consist of a cascade of linear+nonlinear layers that modify the linear measurements at the retinal photo-receptors leading to a…
Synthesizing Visual Illusions Using Generative Adversarial Networks
Alexander Gomez-Villa, Adrian Martín, Javier Vazquez-Corral +2
Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system. In this work we intro…