Scaling lightness perception and differences above and below diffuse white and modifying color spaces for high-dynamic-range scenes and images

The first purpose of this thesis was to design and complete psychophysical experiments for scaling lightness and lightness differences for achromatic percepts above and below the lightness of diffuse white (L*=100). Below diffuse white experiments were conducted under reference conditions recommended by CIE for color difference research. Overall a range of CIELAB lightness values from 7 to 183 was investigated. Psychophysical techniques of partition scaling and constant stimuli were applied for scaling lightness perception and differences, respectively. The results indicate that the existing L* and CIEDE2000-weighting functions approximately predict the trends, but don\u27t well fit the visual data. Hence, three optimized functions are proposed, including a lightness function, a lightness-difference weighting function for the wide range, and a lightness-difference weighting function for the range below diffuse white. The second purpose of this thesis was to modify the color spaces for high-dynamic-range scenes and images. Traditional color spaces have been widely used in a variety of applications including digital color imaging, color image quality, and color management. These spaces, however, were designed for the domain of color stimuli typically encountered with reflecting objects and image displays of such objects. This means the domain of stimuli with luminance levels from slightly above zero to that of a perfect diffuse white (or display white point). This limits the applicability of such spaces to color problems in high-dynamic-range (HDR) imaging. This is caused by their hard intercepts at zero luminance/lightness and by their uncertain applicability for colors brighter than diffuse white. To address HDR applications, two new color spaces were recently proposed by Fairchild and Wyble: hdr-CIELAB and hdr-IPT. They are based on replacing the power-function nonlinearities in CIELAB and IPT with more physiologically plausible hyperbolic functions optimized to most closely simulate the original color spaces in the diffuse reflecting color domain. This thesis presents the formulation of the new models, evaluations using Munsell data in comparison with CIELAB, IPT, and CIECAM02, two sets of lightness-scaling data above diffuse white, and various possible formulations of hdr-CIELAB and hdr-IPT to predict the visual results

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

A Contrast/Filling-In Model of 3-D Lightness Perception

Wallach's ratio hypothesis states that local luminance ratios clr!termine lightness perception under variable illumination. While local luminance ratios successfully discount gradual variations in illumination (illumination constancy or Type I constancy), they fail to explain lightness constancy in general. Some examples of failures of the ratio hypothesis include effects suggesting the coplanar ratio hypothesis (Gilchrist 1977), "assimilation" effects, and configural effects such as the Benary cross, and White's illusion. The present article extends the Boundary Contour System/Feature Contour System (BCS/FCS) approach to provide an explanation of these effects in terms of a neural model of 3-D lightness perception. Lightness constancy of objects in front of different backgrounds (background constancy or Type II constancy) is used to provide functional constraints to the theory and suggest a contrast negation hypothesis which states that ratio measures between coplanar regions are given more weight in the determination of lightness. Simulations of the model applied to several stimuli including Benary cross and White's illusion show that contrast negation mechanisms modulate illumination constancy mechanisms to extend the explanatory power of the model. The model is also used to devise new stimuli that test theoretical predictions

Brilliance, contrast, colorfulness, and the perceived volume of device color gamut

With the advent of digital video and cinema media technologies, much more is possible in achieving brighter and more vibrant colors, colors that transcend our experience. The challenge is in the realization of these possibilities in an industry rooted in 1950s technology where color gamut is represented with little or no insight into the way an observer perceives color as a complex mixture of the observer’s intentions, desires, and interests. By today’s standards, five perceptual attributes – brightness, lightness, colorfulness, chroma, and hue - are believed to be required for a complete specification. As a compelling case for such a representation, a display system is demonstrated that is capable of displaying color beyond the realm of object color, perceptually even beyond the spectrum locus of pure color. All this begs the question: Just what is meant by perceptual gamut? To this end, the attributes of perceptual gamut are identified through psychometric testing and the color appearance models CIELAB and CIECAM02. Then, by way of demonstration, these attributes were manipulated to test their application in wide gamut displays. In concert with these perceptual attributes and their manipulation, Ralph M. Evans’ concept of brilliance as an attribute of perception that extends beyond the realm of everyday experience, and the theoretical studies of brilliance by Y. Nayatani, a method was developed for producing brighter, more colorful colors and deeper, darker colors with the aim of preserving object color perception – flesh tones in particular. The method was successfully demonstrated and tested in real images using psychophysical methods in the very real, practical application of expanding the gamut of sRGB into an emulation of the wide gamut, xvYCC encoding

High Dynamic Range (HDR) Display Perception

Displays have undergone a huge development in the last several decades. From cathode-ray tube (CRT), liquid crystal display (LCD), to organic light-emitting diode (OLED), even Q-OLED, the new configurations of the display bring more and more functions into industry and daily life. In the recent several years, high dynamic range (HDR) displays become popular. HDR displays usually refer to that the black level of the display is darker and the peak being brighter compared with the standard dynamic range (SDR) display. Traditionally, the peak luminance level can be used as the white in characterization and calibration. However, for HDR displays, the peak luminance is higher than the traditional diffuse white level. Exploration of the perceptual diffuse white in HDR image when presented in displays is proposed, which can be beneficial to the characterizing and the optimizing the usage of the HDR display. Moreover, in addition to the ``diffuse white , 3D color gamut volume can be calculated in some specific color appearance models. Calculation and modeling of the 3D color gamut volume can be very useful for display design and better characterizing display color reproduction capability. Furthermore, the perceptional color gamut volume can be measured through psychophysical experiments. Comparison between the perceptional color gamut volume and the theoretical 3D gamut volume calculations will reveal some insights for optimizing the usage of HDR displays. Another advantage of the HDR display is its darker black compared with the SDR display. Compared with the real black object, what level of black is `perfect\u27 enough in displays? Experiments were proposed and conducted to evaluate that if the HDR display is capable of showing ``perfect black for different types of background images/patterns. A glare-based model was proposed to predict the visual ``perfect black. Additionally, the dynamic range of human vision system is very large. However, the simultaneous dynamic range of human vision system is much smaller and is important for the fine tuning usage of HDR displays. The simultaneous dynamic range was measured directly for different stimulus sizes. Also, it was found that the simultaneous dynamic range was peak luminance level dependent. A mathematical model was proposed based on the experimental data to predict the simultaneous dynamic range. Also the spatial frequency effect of the target pattern on the simultaneous dynamic range was measured and modeled. The four different assessments about HDR displays perception would provide experimental data and models for a better understanding of HDR perception and tuning of the HDR display

Measurement, modeling and perception of painted surfaces : A Multi-scale analysis of the touch-up problem

Real-world surfaces typically have geometric features at a range of spatial scales. At the microscale, opaque surfaces are often characterized by bidirectional reflectance distribution functions (BRDF), which describes how a surface scatters incident light. At the mesoscale, surfaces often exhibit visible texture - stochastic or patterned arrangements of geometric features that provide visual information about surface properties such as roughness, smoothness, softness, etc. These textures also affect how light is scattered by the surface, but the effects are at a different spatial scale than those captured by the BRDF. Through this research, we investigate how microscale and mesoscale surface properties interact to contribute to overall surface appearance. This behavior is also the cause of the well-known touch-up problem in the paint industry, where two regions coated with exactly the same paint, look different in color, gloss and/or texture because of differences in application methods. At first, samples were created by applying latex paint to standard wallboard surfaces. Two application methods- spraying and rolling were used. The BRDF and texture properties of the samples were measured, which revealed differences at both the microscale and mesoscale. This data was then used as input for a physically-based image synthesis algorithm, to generate realistic images of the surfaces under different viewing conditions. In order to understand the factors that govern touch-up visibility, psychophysical tests were conducted using calibrated, digital photographs of the samples as stimuli. Images were presented in pairs and a two alternative forced choice design was used for the experiments. These judgments were then used as data for a Thurstonian scaling analysis to produce psychophysical scales of visibility, which helped determine the effect of paint formulation, application methods, and viewing and illumination conditions on the touch-up problem. The results can be used as base data towards development of a psychophysical model that relates physical differences in paint formulation and application methods to visual differences in surface appearance

The Computation of Surface Lightness in Simple and Complex Scenes

The present thesis examined how reflectance properties and the complexity of surface mesostructure (small-scale surface relief) influence perceived lightness in centresurround displays. Chapters 2 and 3 evaluated the role of surface relief, gloss, and interreflections on lightness constancy, which was examined across changes in background albedo and illumination level. For surfaces with visible mesostructure (“rocky” surfaces), lightness constancy across changes in background albedo was better for targets embedded in glossy versus matte surfaces. However, this improved lightness constancy for gloss was not observed when illumination varied. Control experiments compared the matte and glossy rocky surrounds to two control displays, which matched either pixel histograms or a phase-scrambled power spectrum. Lightness constancy was improved for rocky glossy displays over the histogram-matched displays, but not compared to phase-scrambled variants of these images with equated power spectrums. The results were similar for surfaces rendered with 1, 2, 3 and 4 interreflections. These results suggest that lightness perception in complex centre-surround displays can be explained by the distribution of contrast across space and scale, independently of explicit information about surface shading or specularity. The results for surfaces without surface relief (“homogeneous” surfaces) differed qualitatively to rocky surfaces, exhibiting abrupt steps in perceived lightness at points at which the targets transitioned from being increments to decrements. Chapter 4 examined whether homogeneous displays evoke more complex mid-level representations similar to conditions of transparency. Matching target lightness in a homogeneous display to that in a textured or rocky display required varying both lightness and transmittance of the test patch on the textured display to obtain the most satisfactory matches. However, transmittance was only varied to match the contrast of targets against homogeneous surrounds, and not to explicitly match the amount of transparency perceived in the displays. The results suggest perceived target-surround edge contrast differs between homogeneous and textured displays. Varying the mid-level property of transparency in textured displays provides a natural means for equating both target lightness and the unique appearance of the edge contrast in homogeneous displays

Perception of Lighting and Reflectance in Real and Synthetic Stimuli

The human visual system estimates the proportion of light reflected off of a surface despite variable lighting in a scene, a phenomenon known as lightness constancy. Classically, lightness constancy has been explained as a 'discounting' of the lighting intensity (Helmholtz, 1866), and this continues to be a common view today (e.g., Brainard & Maloney, 2011). However, Logvinenko and Maloney (2006) have made a radically different claim that the human visual system does not have any perceptual access to an estimation of lightness. The experiments described in Chapter 2 use a novel experimental paradigm to test this new theory proposed by Logvinenko and Maloney. We provide evidence against Logvinenko and Maloney's theory of lightness perception while adding to existing evidence that the visual system has good lightness constancy. In Chapter 3, we manipulate screen colour and texture cues to test the realism of computer-generated stimuli. We find that by matching the chromaticity of an LCD screen to the surrounding lighting and using a realistic texture, LCD screens can be made to appear similar to physical paper. Finally, Chapter 4 is an extension of the ideas from Chapter 3, in which the knowledge about how to adjust color and texture cues on an LCD monitor is applied to a lightness matching task. Here, the LCD screen is a small part of a larger physical setup. Additionally, levels of lightness constancy are compared across physical and simulated surfaces in the same novel experimental paradigm in Chapters 2 and 4. We find that physical and simulated surfaced elicit different levels of lightness constancy on the same task