The digital representation of three dimensional objects with different materials has become common not only in the games and movie industry, but also in designer software, e-commerce and other applications. Although the rendered images often seem to be realistic, a closer look reveals that their color accuracy is often insufficient for critical applications. Storage of the angledependent color properties of metallic coatings and other gonioapparent materials demands large amounts of data. Apart from that, also rendering sparkle, gloss and other visual texture phenomena is still a subject of active research. Current approaches are computationally very demanding, and require manual ad-hoc setting of many model parameters. In this paper, we describe a new approach to solve these problems. We combine a multi-spectral physics-based approach to make BRDF representation more efficient. We also account for the common loss in color accuracy due to the varying technical specifications of displays, and we correct for the influence from ambient lighting. The rendering framework presented here is shown to be capable of rendering sparkle and gloss as well, based on objective measurement of these properties. This takes out the subjective phase of manual fine-tuning of model parameters that is characteristic for many current rendering approaches. A feasibility test with the new spectral rendering pipeline shows that is indeed able to produce realistic rendering of color, sparkle, gloss and other texture aspects. The computation time is small enough to make the rendering real-time on an iPad 2017, i.e. with low memory footprint and without high demands on graphic card or data storage.
The colors of Van Gogh’s landscape painting Field with Irises near Arles have changed considerably. To digitally reconstruct its original colors, we use an unprecedented broad scientific analysis and experimental art technological approach, by physically reconstructing oil paints of all pigments used by Van Gogh. We closely match the original paints, and for the first time determine all the optical properties involved. The investigation led to a digital image representing the original colors as good as possible.We found that for the digital color reconstruction it is important to take into account that museum lighting is often relatively dark in order to better preserve paintings. Since this affects the best way of representing the reconstructed colors on the display, we adapted the digital reconstructed image. We also corrected for the technical specifications of the electronic display on which the reconstructions will be displayed in the museum.Based on the reconstruction we conclude that the original colors in the painting used to be much brighter, and agreed much better with Van Gogh’s own description of the color composition of this painting. We show that unlike the current colors of the painting, the reconstructed colors are consistent with the color theories on which Van Gogh based his work.
A gamut compression algorithm (GCA) and a gamut extension algorithm (GEA) were proposed based on the concept of vividness. Their performance was further investigated via two psychological experiments together with some other commonly used gamut mapping algorithms (GMAs). In addition, difference uniform colour spaces (UCSs) were also evaluated in the experiments including CIELAB, CAM02-UCS and a newly proposed UCS, Jzazbz. Present results showed that the new GCA and GEA outperformed all the other GMAs and the Jzazbz was a promising UCS in the field of gamut mapping.
Accurately describing the gamut of a color device is the basis for gamut mapping, device color characterization and device gamut volume prediction. There are many ways to describe gamut boundary in the past and the methods can be used in combination with each other for the more accurate and effective gamut boundary description. However, it is difficult to find a commonly used method after introducing the way of color space segmentation. In this paper, we reorganize the existing gamut boundary description techniques according to purpose and method. We also propose a new simple approach of predicting gamut boundary. This new approach uses a machine learning based Radial Basis Function Network(RBFN) that can simplify the gamut boundary description process. This simple method can directly predict the desired gamut boundary description.
In this paper, statistics such as distribution of peak luminance, region of peak luminance in frames, colors of high dynamic range contents are analyzed Based on the analysis, essential requirements for future high dynamic range displays are discussed. For our statistical study, various types of high dynamic range content that have been provided by studios or content providers are considered. Since they have been being supplied by limited studios and network-based content providers, a large amount of the content is movies that utilize limited dynamic range, average luminance and color gamut compared with the other dynamic contents. In spite of the trend, we claim that capability of high dynamic range displays do not need to be restricted by considering the current content industry since very bright high dynamic range contents that have higher luminance and wide color information absolutely need to be also considered when defining specification of future high dynamic range displays. To support this fact, we review the analysis results and requirements which are needed to sufficiently represent vivid high dynamic range presentation to match the human visual perception capability.
Color volumes in the new color spaces ICtCp and Jzazbz are used to characterize the performances of different types of displays. The viewing angle behavior of the emissive properties of one QLED TV and one OLED TVs are measured and compared. The influence of the top polarizer on the reflective properties of one LCD vehicle display is also measured and the color performances under various parasitic illumination are predicted.
Glare is an unwanted scattering of light occurring upon its propagation through optical media, whose scarcely predictable, scene-dependent effects are potentially disrupting in terms of accurate scene acquisition. This work starts from the idea of assessing the magnitude of glare in low dynamic range monitor layers during visualization. According to common practice, monitor dynamic ranges are computed as ratios of maximum to minimum luminance values separately acquired on full-screen black and white images. Avoiding the coexistence in the same image of maximum and minimum luminance, this method does not consider the effect of possible intralayer glare. To measure possible intra-layer glare in a monitor, we have displayed images made up with black and white patterns of different sizes. Measuring these different patterns, we detected changes in the luminance of the black regions. At first we explained data as a glare effect. Measuring more carefully each regions through a masking cardboard with a hole, these differences were no more there. It was just glare, not from the monitor layers, but from the lens of the measuring instrument. To further investigate the issue, another setup was arranged whereby two color checkers were stationed behind a dimmable light source aiming away from them both, and directly into the luminance meter. We found that despite light being unable to fall directly on the color checkers, an increase of radiant power was paralleled by an upward drift in luminance values for all examined spots, more so for those lying the closest to a prominent lens flare within the device viewing field. These combined findings show us that no matter the accuracy of the measuring device, luminance information can neither be measured nor displayed correctly in the presence of glare in the instrument.
Color Constancy has two hypothetical mechanisms: Chromatic Adaptation and Spatial Comparisons. These mechanisms have different fundamental properties. Adaptation models work with small individual scene segments. They combine radiance measurements of individual segments with the modeler’s selected parameters that scale the receptor’s cone quanta catches. Alternatively, spatial models use the radiance map of the entire field of view to calculate appearances of all image segments simultaneously. They achieve independence from spectral shifts in illumination by making spatial comparisons within each L, M, S color channel. These spatial comparisons respond to color crosstalk caused by the overlap of spectral sensitivities of cone visual pigments. L, M, and S cones respond to every visible wavelength. Crosstalk causes the spatial comparisons of cone responses to vary with changes in spectral illumination. Color Constancy works best in spatially uniform, and variable spectral illumination. Measurements of Color Constancy show systematic departures from perfect constancy. These limits of Color Constancy are predicted by spatial comparisons with cone Crosstalk. These limits do not correlate with Chromatic Adaptation models. This paper describes cone Crosstalk, and reviews a series of measurements of the limits of Color Constancy in variable spectral, spatial and real-life illuminations.
Over the years, there have been many studies conducted for whiteboard image detection, extraction and quality enhancements. However, the image quality attributes of the streaming whiteboard contents as well as users’ expectations from such whiteboard scenes are not well investigated. Therefore, the primary goal of this work is to examine the effects of the different whiteboard image features on the overall quality of the whiteboard images. Particularly, the naturalness and legibility quality attributes of the images were investigated through psychovisual experiments. Our experimental results show that increasing color attributes such as saturation, brightness and luminance contrast, lead to more legible whiteboard contents; which in turn increases the whiteboard image quality. Enhancement processes of the whiteboard backgrounds, however, show strong effects on the naturalness attribute. But, when the general image quality is considered, observers tend to prefer more legible whiteboard image contents rather than the naturalness of the appearance.