Color imaging has historically been treated as a phenomenon sufficiently described by three independent parameters. Recent advances in computational resources and in the understanding of the human aspects are leading to new approaches that extend the purely metrological view of color towards a perceptual approach describing the appearance of objects, documents and displays. Part of this perceptual view is the incorporation of spatial aspects, adaptive color processing based on image content, and the automation of color tasks, to name a few. This dynamic nature applies to all output modalities, including hardcopy devices, but to an even larger extent to soft-copy displays with their even larger options of dynamic processing. Spatially adaptive gamut and tone mapping, dynamic contrast, and color management continue to support the unprecedented development of display hardware covering everything from mobile displays to standard monitors, and all the way to large size screens and emerging technologies. The scope of inquiry is also broadened by the desire to match not only color, but complete appearance perceived by the user. This conference provides an opportunity to present, to interact, and to learn about the most recent developments in color imaging and material appearance researches, technologies and applications. Focus of the conference is on color basic research and testing, color image input, dynamic color image output and rendering, color image automation, emphasizing color in context and color in images, and reproduction of images across local and remote devices. The conference covers also software, media, and systems related to color and material appearance. Special attention is given to applications and requirements created by and for multidisciplinary fields involving color and/or vision.

Lightness Illusions (Contrast, Assimilation, and Natural Scenes with Edges and Gradients) show that Lightness appearances do not correlate with the light sent from the scene to the eye. Illusions modify “the-rest-of-the-scene” to make two identical-luminance Gray segments appear different from each other. Scene segments have two properties in human vision: apparent Lightness, and apparent Uniformity. Models of vision have two scene-dependent processes that spatially transform scene luminances. The first is optical veiling glare that modifies the sharpness of the edges, and replaces uniform scene segments with low-slope gradients. The second scene-dependent transformation is neural spatial processing. This means that this spatial transformation has many tasks to perform in generating appearances. They include: making edges appear sharp; making gradients in scene segments appear uniform; and compensating for glare’s many local redistributions of light. In short, neural spatial processing does an excellent job of ignoring glare’s distortions of scene luminance. In fact it over compensates glare in a way that generates appearances reported in Contrast Illusions, B&W Mondrians, and Checkershadow Illusions.

Board game industry is experiencing a strong renewed interest. In the last few years, about 4000 new board games have been designed and distributed each year. Board game players gender balance is reaching the equality, but nowadays the male component is a slight majority. This means that (at least) around 10% of board game players are color blind. How does the board game industry deal with this ? Recently, a raising of awareness in the board game design has started but so far there is a big gap compared with (e.g.) the computer game industry. This paper presents some data about the actual situation, discussing exemplary cases of successful board games.

In the last 80 years, the role of spatial processing in the visual system has been analyzed and demonstrated from many studies and experiments. Starting from the first studies of Young, Helmholtz and Hering, color vision models have developed, and several biological and physiological research paper proved the importance of spatial processing in color vision. In this paper, we present some of the studies which have explored the role of spatial processing to study color vision deficiency. Main scope of this work is to increase the awareness of the scientific community on the importance to include spatial processing not only in color vision models, but also in developing color deficiency aids and tests.

In this study, the brightness matching experiment was conducted to obtain the equivalent luminance between chromatic and achromatic colors. Observers adjusted the luminance of achromatic colors until achromatic colors were perceived as having the same brightness with chromatic colors. A total of 285 chromatic colors having three different luminance levels, 30cd/m^2, 95cd/m^2, and 300cd/m^2 were used as the test colors. Twenty observers participated in this experiment repeating three times. The results showed that the brightness-to-luminance (B/L) ratio, where brightness means the luminance of achromatic color, increases as CIE 1976 saturation increases in all luminance levels indicating the Helmholtz-Kohlrausch effect. Also, as the luminance level of chromatic color increases, B/L ratio decreases. It is found that the existing color appearance models predicting the H-K effect overestimate the brightness increment by chroma compared to our new heterochromatic brightness matching data set.

Natural image statistics are well known to have a spatial frequency power spectra that has a 1/f^a behavior, with a typically stated as between 2 and 4. This indicates an invariance to scale. Further work has theorized how the visual system is tuned for such statistics in visual cortex (V1) [1]. Color image statistics also show an invariance to scale [2]. The luminance histogram is typically understood to be log normal with respect to luminance, although for HDR images, a subcomponent with skew toward much higher luminances is observed. Color statistics were initially described at the simplest level via the gray world hypothesis [3], but more details are now available, even at the hyperspectral [4]. The a power function for HDR was found to increase from the lower values of 2 to more typical values of 4 and 5 [5]. For temporal statistics, the data tends to be measured primarily for media, with a 1/f^a for scene cut statistics [6], and temporal frequency and temporal frequency for media with a focus on the motion statistics via optical flow [7]. Statistics for purely natural as well as human made environments (e.g., buildings and the resulting perspective geometry) have been studied, each having different orientation statistics [8]. The use of image statistics for standardized assessment of television power consumption was used to replace test targets, which were often detected and used to lower TV power consumption in well known cheating schemes. To prevent this, a short test video that had luminance statistics matching 48 hours of broadcast content was generated and used for TV power testing [9]. The highly adaptative nature of current TVs (power limiting, dual modulation, dynamic response) has motivated researchers to incorporate complex noise fields following natural image statistics into measurement targets [10,11]. One particular natural image statistic-based still image test target (dead leaves) is widely used in camera optics and sensor development. Algorithm development and testing for image and video processing has almost always been ad hoc, with a mixture of geometric test targets and hand selected test images, sometimes aiming to be corner cases, sometimes not. More recently, large data sets of images have been used to train various neural network models for tasks such as super resolution, bit rate compression, and dynamic range mapping. However, images are not ergodic, and possibly not even wide-sense stationary. We propose the use of imagery based on noise following the natural image statistics for spatio-chromatic (and temporal) to compactly probe the wide variety of image possibilities for algorithmic development, in addition to the existing uses for image capture and display analysis. While we don’t suggest replacing actual practical imagery, we believe such noise fields can augment image algorithm analysis. To address the problem of non-ergodicity, we allow the basic power term a in the natural image statistic model to vary over a large range in a video, such that it includes the extremes of white noise and low frequency gradients. We use color image statistic models that include decorrelated colors to generate the RGB video. We will present results for traditional adaptive data compression (with chromatic subsampling), as well as a more contemporary neural network approach (Neural Fields [12]) as applied to upscaling and denoising. We analyze the results both visually and through several recent color image quality models. Field DJ. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A, 1987; 4:2379-2394 C. Parraga, T. Troscianko, and D.J. Tolhurst (2002) spatiochromatic properties of natural images and human vision. Current Biology V 12 R. M. Evans, Method for correcting photographic color prints, US Patent 2,571,697 (1951) A. Chakrabarti and T. Zickler (2011) Statistics of real-world hyperspectral images CVPR R. Dror, A. Willsky, and E. Adelson (2004) statistical characterization of real-world illumination. JOV V4 J. Cutting (2019) Sequences in popular cinema generate inconsistent event segmentation. Attn. Percept. And Psycho. V 81. D. Lee, H. Ko, J. Kim, and A. Bovik (2021) On the space-time statistics of motion pictures. JOSA A V 38 #7 A. Torralba and A. Oliva (2003) Statistics of natural image categories, Network: Computational Neural Systems 14 391-412 International Electrotechnical Commission, IEC 62087:2008(E), “Methods of measurement for the power consumption of audio, video, and related Equipment. Kunkel T, Daly S. 57-1: Spatiotemporal Noise Targets Inspired by Natural Imagery Statistics. SID Symposium Digest of Technical Papers, 2020, 51:842-845. Kunkel, T, Friedrich, F. Utilizing advanced spatio-temporal backgrounds with dynamic test signals for high dynamic range display metrology. J Soc Inf Display. 2022; 30( 5): 423– 432. https://doi.org/10.1002/jsid.1125 Yiheng Xie1, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, Srinath Sridhar1, "Neural Fields in Visual Computing and Beyond", Eurographics / CGF State-of-the-Art Report, 2022.

The end of life is a good time to look back to what I have learned in the past 50 years and share my lessons. Except for stints in engineering and marketing, I have worked mostly in research labs. Although I was mostly in an imaging lab, de facto my research has been primarily in color science. I have worked in industry, but on the side I have volunteered for national science foundations, learned societies, and patent offices. The main take-away is that life in research is not smooth, you have to be resilient to set-backs, and be well connected.

The fluorescence property of human teeth under UV light has long been studied in dentistry and is now used in the diagnosis of anomalies, such as dental decays. Its role in the appearance of teeth and dental restorations has also been demonstrated, and fluorescence, even under daylight, may sensibly modify the color of dental restorations. As such, dental resin composites used in aesthetic restorative dentistry include fluorescent agents which aim to reproduce the natural fluorescence of teeth. While several studies have measured the fluorescence properties of dental biomaterials and a few other studies have focused on predicting the color of samples, the influence of fluorescence on color prediction models remains to be assessed. In this paper, we propose a prediction model for the spectral emission of slices of a dental biomaterial as a function of their thicknesses, in reflection and in transmission modes, in order to improve color prediction models for these materials.

Gloss perception is a complex psychovisual phenomenon, whose mechanisms are not yet fully explained. Instrumentally measured surface reflectance is usually poor predictor of human perception of gloss. The state-of-the-art studies demonstrate that, in addition to surface reflectance, object's shape and illumination geometry also affect the magnitude of gloss perceived by the human visual system (HVS). Recent studies attribute this to image cues – the specific regularities in image statistics that are generated by a combination of these physical properties, and that, in their part, are proposedly used by the HVS for assessing gloss. Another study has recently demonstrated that subsurface scattering of light is an additional factor that can play the role in perceived gloss, but the study provides limited explanation of this phenomenon. In this work, we aimed to shed more light to this observation and explain why translucency impacts perceived gloss, and why this impact varies among shapes. We conducted four psychophysical experiments in order to explore whether image cues typical for opaque objects also explain the variation of perceived gloss in translucent objects and to quantify how these cues are modulated by the subsurface scattering properties. We found that perceived contrast, coverage area, and sharpness of the highlights can be combined to reliably predict perceived gloss. While sharpness is the most significant cue for assessing glossiness of spherical objects, coverage is more important for a complex Lucy shape. Both of these observations propose an explanation why subsurface scattering albedo impacts perceived gloss.

A cross polarization could be indispensable in certain applications when scanning and digitizing highly reflective materials or when certain applications couldn’t afford following the recommended imaging geometry 0⁰/45⁰ | 45^o/0^o for some technical reasons. However, that puts very much color fidelity in question, to which extent a cross polarization may impact the source illuminant in the first place that is consequently impacting the color appearance during the imaging and the color correction procedures. In this research we show how certain cross polarization setups are adding a chroma tint to the light source, D50 in this study, causing by that undesirable color shift of the color of the light source. Consequently, a shift in its color correlated temperature moving, in worst case scenario, from ~5000K to ~4500K and resulting in an increased DE00 as a result of the added chroma when compared against a standard D50; nearly doubled in best case scenario and nearly tripled in worst case scenario.