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

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

Modeling Perceptual Trade-offs for Designing HDR Displays

Display technology has evolved in pursuit of perceptual pleasure by providing realism and visual impact. The endeavor of the evolution has brought HDR displays to the market. HDR displays, which have become the mainstream display technology recently, are considered not only the present but also the future of displays because of their daunting technical goals: A peak luminance of 10,000 cd/m^2 and near-monochromatic primaries. However, both positive and negative prospects in terms of perceptual aspects for future HDR displays coexist. On the positive side, it is expected that HDR displays will provide better image quality and more vivid color. On the negative side, apart from technical barriers such as production cost and power consumption, HDR displays will induce side effects, for example, observer metamerism, which refers to the phenomenon that color matches for one observer result in color mismatches for other observers. This particular side effect could be a severe issue in HDR displays as their narrow-band primaries likely worsen the color mismatches. Hence, critical to the success of future HDR displays is dealing properly with the perceptual trade-offs. In other words, future HDR display designers need to select physical specifications that maximize perceptual benefits while minimizing adverse effects. This dissertation aims at exploring both potentially positive and negative aspects of future HDR displays, using various perceptual assessments. In particular, the dissertation focuses on two physical factors of a display device: peak luminance and chromaticity color gamut, and the effects of the two factors on related human perception: image quality, observer metamerism, and colorfulness. The ultimate goal of this dissertation is to address the related human perception aroused by the physical factors and propose models to help design future HDR displays. In order to achieve the goal, the dissertation first addresses the image quality trade-off relationship between peak luminance and chromaticity color gamut. A psychophysical experiment was used to develop models to predict equivalent image quality under the trade-off between peak luminance and chromaticity gamut as a function of the perceptual attributes lightness and chroma. Second, a novel approach based on a computational evaluation to investigate potential observer metamerism in HDR displays was explored. This research shows how observer metamerism in HDR displays varies with varying peak luminance and chromaticity color gamut. This research aims at developing a straightforward model to predict observer metamerism in HDR displays based on the computational evaluation. Third, a psychophysical experiment to derive a colorfulness scale for very saturated colors is carried out. This experiment focuses on understanding how the sensitivity of the human visual system responds to highly-saturated colors that extend beyond the stimuli studied in previous research. The colorfulness scale would help both advanced lighting system and display system designers. Fourth, the dissertation suggests an evaluation tool devised based on the observer metamerism and colorfulness scale works that can be utilized to determine the physical specification of HDR displays, maximizing perceptually positive effects while minimizing perceptually negative effects at the same time

Spectral imaging of human portraits and image quality

This dissertation addresses the problem of capturing spectral images for human portraits and evaluating image quality of spectral images. A new spectral imaging approach is proposed in this dissertation for spectral images of human portraits. Thorough statistical analysis is performed for spectral reflectances from various races and different face parts. A spectral imaging system has been designed and calibrated for human portraits. The calibrated imaging system has the ability to represent not only the facial skin but also the spectra of lips, eyes and hair from various races as well. The generated spectral images can be applied to color-imaging system design and analysis. To evaluate the image quality of spectral imaging systems, a visual psychophysical image quality experiment has been performed in this dissertation. The spectral images were simulated based on real spectral imaging system. Meaningful image quality results have been obtained for spectral images generated from different spectral imaging systems. To bridge the gap between the physical measures and subjective visual perceptions of image quality, four image distortion factors were defined. Image quality metrics were obtained and evaluated based statistical analysis and multiple analysis. The image quality metrics have high correlation with subjective assessment for image quality. The image quality contribution of the distortion factors were evaluated. As an extension of the work other researchers in MCSL have initiated, this dissertation research will, working with other researchers in MCSL, put effort to build a publicly accessible database of spectral images, Lippmann2000

Lightness, Brightness, and Transparency in Optical See-Through Augmented Reality

Augmented reality (AR), as a key component of the future metaverse, has leaped from the research labs to the consumer and enterprise markets. AR optical see-through (OST) devices utilize transparent optical combiners to provide visibility of the real environment as well as superimpose virtual content on top of it. OST displays distinct from existing media because of their optical additivity, meaning the light reaching the eyes is composed of both virtual content and real background. The composition results in the intended virtual colors being distorted and perceived transparent. When the luminance of the virtual content decreases, the perceived lightness and brightness decrease, and the perceived transparency increases. Lightness, brightness, and transparency are modulated by one physical dimension (luminance), and all interact with the background and each other. In this research, we aim to identify and quantify the three perceptual dimensions, as well as build mathematical models to predict them. In the first part of the study, we focused on the perceived brightness and lightness with two experiments: a brightness partition scaling experiment to build brightness scales, and a diffuse white adjustment experiment to determine the absolute luminance level required for diffuse white appearances on 2D and 3D AR stimuli. The second part of the research targeted at the perceived transparency in the AR environment with three experiments. The transparency was modulated by the background Michelson contrast reduction in either average luminance or peak-to-peak luminance difference to investigate, and later illustrated, the fundamental mechanism evoking transparency perception. The first experiment measured the transparency detection thresholds and confirmed that contrast sensitivity functions with contrast adaptation could model the thresholds. Subsequently, the transparency perception was investigated through direct anchored scaling experiment by building perceived transparency scales from the virtual content contrast ratio to the background. A contrast-ratio-based model was proposed predicting the perceived transparency scales. Finally, the transparency equivalency experiment between the two types of contrast modulation confirmed the mechanism difference and validated the proposed model

Appearance-based image splitting for HDR display systems

High dynamic range displays that incorporate two optically-coupled image planes have recently been developed. This dual image plane design requires that a given HDR input image be split into two complementary standard dynamic range components that drive the coupled systems, therefore there existing image splitting issue. In this research, two types of HDR display systems (hardcopy and softcopy HDR display) are constructed to facilitate the study of HDR image splitting algorithm for building HDR displays. A new HDR image splitting algorithm which incorporates iCAM06 image appearance model is proposed, seeking to create displayed HDR images that can provide better image quality. The new algorithm has potential to improve image details perception, colorfulness and better gamut utilization. Finally, the performance of the new iCAM06-based HDR image splitting algorithm is evaluated and compared with widely spread luminance square root algorithm through psychophysical studies

Ridge Regression Approach to Color Constancy

This thesis presents the work on color constancy and its application in the field of computer vision. Color constancy is a phenomena of representing (visualizing) the reflectance properties of the scene independent of the illumination spectrum. The motivation behind this work is two folds:The primary motivation is to seek ‘consistency and stability’ in color reproduction and algorithm performance respectively because color is used as one of the important features in many computer vision applications; therefore consistency of the color features is essential for high application success. Second motivation is to reduce ‘computational complexity’ without sacrificing the primary motivation.This work presents machine learning approach to color constancy. An empirical model is developed from the training data. Neural network and support vector machine are two prominent nonlinear learning theories. The work on support vector machine based color constancy shows its superior performance over neural networks based color constancy in terms of stability. But support vector machine is time consuming method. Alternative approach to support vectormachine, is a simple, fast and analytically solvable linear modeling technique known as ‘Ridge regression’. It learns the dependency between the surface reflectance and illumination from a presented training sample of data. Ridge regression provides answer to the two fold motivation behind this work, i.e., stable and computationally simple approach. The proposed algorithms, ‘Support vector machine’ and ‘Ridge regression’ involves three step processes: First, an input matrix constructed from the preprocessed training data set is trained toobtain a trained model. Second, test images are presented to the trained model to obtain the chromaticity estimate of the illuminants present in the testing images. Finally, linear diagonal transformation is performed to obtain the color corrected image. The results show the effectiveness of the proposed algorithms on both calibrated and uncalibrated data set in comparison to the methods discussed in literature review. Finally, thesis concludes with a complete discussion and summary on comparison between the proposed approaches and other algorithms

Engineering Data Compendium. Human Perception and Performance, Volume 1

The concept underlying the Engineering Data Compendium was the product an R and D program (Integrated Perceptual Information for Designers project) aimed at facilitating the application of basic research findings in human performance to the design of 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 existing research literature, and (2) presenting this technical information in a way that would aid its accessibility, interpretability, and applicability by system designers. The present four volumes of the Engineering Data Compendium represent the first implementation of this strategy. This is Volume 1, which contains sections on Visual Acquisition of Information, Auditory Acquisition of Information, and Acquisition of Information by Other Senses

Underlying elements of image quality assessment: : Preference and terminology for communicating image quality characteristics

Image quality markedly affects the evaluation of images, and its control is crucial in studies using natural visual scenes as stimuli. Various image elements, such as sharpness or naturalness, can impact how observers view images and more directly how they evaluate their quality. To gain a better understanding of the types of interactions between these various elements, we conducted a study with a large set of images with multiple overlapping distortions, covering a wide range of quality variation. Observers assigned a quality rating on a 0-10 scale plus a verbal description of the images, explaining the elements on which their rating was based. Regression model predicting image quality ratings using 68 attributes uncovered the link between verbal descriptions and quality ratings and the importance of the image quality rating for each of the 68 image attributes. Brightness, naturalness, and good colors seem to be related to the highest image quality preference. However, the most important elements for predicting good image quality were related to image fidelity such as graininess and sharpness. This indicates that a certain level of image fidelity must be achieved before more subjective associations with, for instance, naturalness can emerge. Of the attributes, 72% had a negative impact on the preference judgment. This negative bias may be due to the fact that there are more ways that observers can perceive an image to fail than to excel when they are asked to evaluate image quality.Image quality markedly affects the evaluation of images, and its control is crucial in studies using natural visual scenes as stimuli. Various image elements, such as sharpness or naturalness, can impact how observers view images and, more directly, how they evaluate their quality. To gain a better understanding of the types of interactions between these various elements, we conducted a study with a large set of images with multiple overlapping distortions, covering a wide range of quality variation. Observers assigned a quality rating of the images on a 0–10 scale and gave a verbal description explaining the elements on which their rating was based. A regression model predicting image quality ratings using 68 attributes uncovered the link between verbal descriptions and quality ratings and the importance of the image quality rating for each of the 68 image attributes. Brightness, naturalness, and good colors seem to be related to the highest image quality preference. However, the most important elements for predicting good image quality were related to image fidelity such as graininess and sharpness. This indicates that a certain level of image fidelity must be achieved before more subjective associations with, for instance, naturalness can emerge. Of the attributes, 72% had a negative impact on the preference judgment. This negative bias may be due to the fact that there are more ways that observers can perceive an image to fail than to excel when they are asked to evaluate image quality.Peer reviewe