Information Flow in Color Appearance Neural Networks

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 an internal (nonlinear) representation of color that correlates with psychophysical experience. The basic layers of these networks include: (1) chromatic adaptation (normalization of the mean and covariance of the color manifold), (2) change to opponent color channels (PCA-like rotation in the color space), and (3) saturating nonlinearities to get perceptually Euclidean color representations (similar to dimensionwise equalization). The Efficient Coding Hypothesis argues that these transforms should emerge from information-theoretic goals. In case this hypothesis holds in color vision, the question is, what is the coding gain due to the different layers of the color appearance networks? In this work, a representative family of Color Appearance Models is analyzed in terms of how the redundancy among the chromatic components is modified along the network and how much information is transferred from the input data to the noisy response. The proposed analysis is done using data and methods that were not available before: (1) new colorimetrically calibrated scenes in different CIE illuminations for proper evaluation of chromatic adaptation, and (2) new statistical tools to estimate (multivariate) information-theoretic quantities between multidimensional sets based on Gaussianization. Results confirm that the Efficient Coding Hypothesis holds for current color vision models, and identify the psychophysical mechanisms critically responsible for gains in information transference: opponent channels and their nonlinear nature are more important than chromatic adaptation at the retina

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

A Matrix Based Approach for Color Transformations in Reflections

In this thesis, I demonstrate the feasibility of linear regression with 4 × 4 matrices to perform color transformations, specifically looking at the case of color transformations in reflections. I compare and analyze the power and performance linear regression models based on 3 × 3 and 4 × 4 matrices. I conclude that using 4 × 4 matrices in linear regression is more advantageous in power and performance over using 3 × 3 matrices in linear regressions, as 4 × 4 matrices allow for categorically more transformations by including the possibility of translation. This provides more general affine transformations to a color space, rather than being restricted to passing through the origin. I examine the benefits of allowing for negative elements in color transformation matrices. I also touch on the possible differences in application between filled 4 × 4 matrices and diagonal 4 × 4 matrices, and discuss the limitations inherent to linear regression used in any type of matrix operations

Variational models for color image processing in the RGB space inspired by human vision Mémoire d'Habilitation a Diriger des Recherches dans la spécialité Mathématiques

La recherche que j'ai développée jusqu'à maintenant peut être divisée en quatre catégories principales : les modèles variationnels pourla correction de la couleur basée sur la perception humaine, le transfert d'histogrammes, le traitement d'images à haute gammedynamique et les statistiques d'images naturelles en couleur. Les sujets ci-dessus sont très inter-connectés car la couleur est un sujetfortement inter-disciplinaire

Learning efficient image representations: Connections between statistics and neuroscience

This thesis summarizes different works developed in the framework of analyzing the relation between image processing, statistics and neuroscience. These relations are analyzed from the efficient coding hypothesis point of view (H. Barlow [1961] and Attneave [1954]). This hypothesis suggests that the human visual system has been adapted during the ages in order to process the visual information in an efficient way, i.e. taking advantage of the statistical regularities of the visual world. Under this classical idea different works in different directions are developed. One direction is analyzing the statistical properties of a revisited, extended and fitted classical model of the human visual system. No statistical information is used in the model. Results show that this model obtains a representation with good statistical properties, which is a new evidence in favor of the efficient coding hypothesis. From the statistical point of view, different methods are proposed and optimized using natural images. The models obtained using these statistical methods show similar behavior to the human visual system, both in the spatial and color dimensions, which are also new evidences of the efficient coding hypothesis. Applications in image processing are an important part of the Thesis. Statistical and neuroscience based methods are employed to develop a wide set of image processing algorithms. Results of these methods in denoising, classification, synthesis and quality assessment are comparable to some of the most successful current methods

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

Spectrally Based Material Color Equivalency: Modeling and Manipulation

A spectrally based normalization methodology (Wpt normalization) for linearly transforming cone excitations or sensor values (sensor excitations) to a representation that preserves the perceptive concepts of lightness, chroma and hue is proposed resulting in a color space with the axes labeled W , p, t. Wpt (pronounced “Waypoint ) has been demonstrated to be an effective material color equivalency space that provides the basis for defining Material Adjustment Transforms that predict the changes in sensor excitations of material spectral reflectance colors due to variations in observer or illuminant. This is contrasted with Chromatic Adaptation Transforms that predict color appearance as defined by corresponding color experiments. Material color equivalency as provided by Wpt and Wpt normalization forms the underlying foundation of this doctoral research. A perceptually uniform material color equivalency space (“Waypoint Lab or WLab) was developed that represents a non-linear transformation of Wpt coordinates, and Euclidean WLab distances were found to not be statistically different from ∆E⋆94 and ∆E00 color differences. Sets of Wpt coordinates for variations in reflectance, illumination, or observers were used to form the basis of defining Wpt shift manifolds. WLab distances of corresponding points within or between these manifolds were utilized to define metrics for color inconstancy, metamerism, observer rendering, illuminant rendering, and differences in observing conditions. Spectral estimation and manipulation strategies are presented that preserve various aspects of “Wpt shift potential as represented by changes in Wpt shift manifolds. Two methods were explored for estimating Wpt normalization matrices based upon direct utilization of sensor excitations, and the use of a Wpt based Material Adjustment Transform to convert Cone Fundamentals to ”XYZ-like Color Matching Functions was investigated and contrasted with other methods such as direct regression and prediction of a common color matching primaries. Finally, linear relationships between Wpt and spectral reflectances were utilized to develop approaches for spectral estimation and spectral manipulation within a general spectral reflectance manipulation framework – thus providing the ability to define and achieve “spectrally preferred color rendering objectives. The presented methods of spectral estimation, spectral manipulation, and material adjustment where utilized to: define spectral reflectances for Munsell colors that minimize Wpt shift potential; manipulate spectral reflectances of actual printed characterization data sets to achieve colorimetry of reference printing conditions; and lastly to demonstrate the spectral estimation and manipulation of spectral reflectances using images and spectrally based profiles within an iccMAX color management workflow

Engineering data compendium. Human perception and performance. User's guide

The concept underlying the Engineering Data Compendium was the product of a research and development program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design and military crew systems. The principal objective was to develop a workable strategy for: (1) identifying and distilling information of potential value to system design from the existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by systems designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is the first volume, the User's Guide, containing a description of the program and instructions for its use