Coating a printed surface with a smooth transparent layer can modify its color. This is due to light interreflections within the coating layer which produce a halo-shaped point spread function. The change of color is related to the coating thickness and the halftone screening used for printing. Thanks to an optical model able to predict the spectral reflectance of the coated print from the one of the non-coated print, we propose to study the impact of the halftone pattern (shape and profile) on the color change caused by the coating layer. It was found that line halftone patterns with a crenel profile induces the strongest changes of color. This is therefore the pattern that we use for an innovative application of this phenomenon: revealing a binary image by adding or removing a coating layer on the print that is originally uniform.
The iridescence effect, produced by structural color, is difficult (if not impossible) to capture and print using traditional CMYK pigments. RGB pigments, nonetheless, generate structural colors by light interference. The layered surface structure generated by pigments’ particles reflects different wavelengths of light in different viewing angles. In printed media, pigments’ particles will collectively influence the optical response of the surface, depending on their size, orientation, structure, and dimensions, ultimately, affecting the visual characteristics of the image perceived by the observer. In this work, we have studied the influence of different halftones’ structures on printed images, produced with RGB inks via screen printing. We investigated the influence of different halftones’ structures in creating different spatial combinations of inks on the printed surface that reproduce the characteristics of iridescent effect of a headdress made of quetzal feathers. We applied first-order, second-order, and structure-aware FM halftones to compare how they influence the reproduction of the material qualities of the object represented in the image. The results show that the structure-ware halftones improve the representation of the image structures and details and, therefore, it could better convey the 3D surface features that produce iridescence effect in the original feathers of the headdress.
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.
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.
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.
Structure-aware halftoning algorithms aim at improving their non-structure-aware version by preserving high-frequency details, structures, and tones and by employing additional information from the input image content. The recently proposed achromatic structure-aware Iterative Method Controlling the Dot Placement (IMCDP) halftoning algorithm uses the angle of the dominant line in each pixel's neighborhood as supplementary information to align halftone structures with the dominant orientation in each region and results in sharper halftones, gives a more three-dimensional impression, and improves the structural similarity and tone preservation. However, this method is developed only for monochrome halftoning, the degree of sharpness enhancement is constant for the entire image, and the algorithm is prohibitively expensive for large images. In this paper, we present a faster and more flexible approach for representing the image structure using a Gabor-based orientation extraction technique which improves the computational performance of the structure-aware IMCDP by an order of magnitude while improving the visual qualities. In addition, we extended the method to color halftoning and studied the impact of orientation information in different color channels on improving sharpness enhancement, preserving structural similarity, and decreasing color reproduction error. Furthermore, we propose a dynamic sharpness enhancement approach, which adaptively varies the local sharpness of the halftone image based on different textures across the image. Our contributions in the present work enable the algorithm to adaptively work on large images with multiple regions and different textures. (C) 2022 Society for Imaging Science and Technology.
A dot profile model to compensate dot shape irregularity errors of inkjet printers is proposed. Previous tabular approaches for parameterizing the printer model rely on the measurements of the gray level of various printed halftone patterns. However, lots of patterns need to be printed and scanned if the printer generates large drops of colorant. To solve this problem, we propose to simulate the appearance of the rendered patterns so that the model parameters can be computed analytically. The simulation uses the mean dot as the printer dot profile and saturated addition to resolve dot overlap. Besides, we incorporate a standard definition (SD) and a high definition (HD) equivalent gray-scale representation of the printed halftone image produced by the dot profile model into the direct binary search (DBS) algorithm. Experimental results show great improvement in the mid-tone and shadow regions over the printed image halftoned by the original DBS. The HD model further enhances details in the shadows.
No further details about this work can be provided at this time, since a patent may be filed prior to the start of the conference on 15 January 2023 to protect the technology.
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.
No further details about this work can be provided at this time, since a patent may be filed prior to the start of the conference on 15 January 2023 to protect the technology.
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.
Halftoning a continuous-tone image inherently results in loss of information, which makes the inverse process, descreening, a challenging problem. Current state-of-the-art descreening algorithms have two issues: first, they mostly are PSNR-oriented reconstruction algorithms, which tend to generate piecewise smooth images that do not appear realistic due to their lack of texture. Furthermore, these algorithms are typically trained with halftone images generated from the Floyd-Steinberg error diffusion algorithm, which is not an optimal choice since the algorithm is known to generate visible artifacts in the halftone image. We address these issues by the following: first, we propose a new descreening algorithm based on conditional generative adversarial networks (cGAN) that generate descreened images with abundant texture resulting in more realistic appearance. Next, we propose using the direct binary search (DBS) algorithm instead of Floyd-Steinberg error diffusion for generating the halftone images, since it is known to generate halftone images without visible artifacts. Both qualitative and quantitative comparisons show that our algorithm outperforms state-of-the-art descreening algorithms significantly.
Consumer color cameras employ sensors that do not mimic human cone spectral sensitivities, and more generally do not meet the Luther condition since the accompanying color correction substantially amplifies noise in the red channel. This begs the question: if cone spectral sensitivities yield low SNR, why has the Human Visual System so evolved? We answer the above question by noting that since modern ISPs - and the ancient HVS - remove virtually all chrominance noise, chrominance denoising artifacts rather than the chrominance noise itself should be considered. While sensor green, blue are reasonable analogs of the human M, S cones, the spectral sensitivity of red is much narrower than that of L and does not overlap much with green. An imager employing L instead of red suffers from increased red noise but is also more sensitive. This allows a high SNR (L + M)/2 luminance image to be reconstructed and used for denoising. Modeling the color filter array on the human retina, with a higher density of L pixels at the expense of S pixels, further improves the red SNR without the accompanying loss of blue quality being perceptible. The resulting LMS camera outperforms conventional RGB cameras in color accuracy and luminance SNR while being competitive in chrominance quality.
Recent advanced light systems offer light presets to enable users to navigate a proper light. Often, the presets are labeled with target ambiance or mood, yet ambiguous for users to predict the light. This study proposes an algorithm that matches the light presets based on the image color characteristics derived from a photograph taken by users. In particular, we developed an automatic match of a light preset for an electric vitrine in which 15 RGB LEDs were linearly arrayed beneath the cover. We conducted a creativity workshop with eight light designers and composed a pool of 22 light presets for the electric vitrine. The presets attempted to cover five standard illuminants, eleven chromatic lights, and six kinds of the color spectrum. The algorithm enables users to receive the optimally matched light presets in order of hue similarities between displayed objects and the light preset. Based on stakeholders’ feedback, the algorithm UX is designed to provide three alternatives made up of the best chromatic light color, the best standard illuminant, and the best color spectrum. We expect the algorithm to be applied to different contexts, where light needs to be optimally tuned by being aware of the context characteristics.
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 | 45o/0o 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.
Two-and-a-half and 3D printing are becoming increasingly popular, and consequently the demand for high quality surface reproduction is also increasing. Halftoning plays an important role in the quality of the surface reproduction. Three dimensional halftoning methods, that adapt the halftone structures to the geometrical structure of 3D surfaces or to the viewing direction, could further improve surface reproduction quality. In this paper, a 3D adaptive halftoning method is proposed, that incorporates different halftone structures on the same 3D surface. The halftone structures are firstly adapted to the 3D geometrical structure of the surface. Secondly, the halftone structures are adapted based on the normal vector to the surface at a specific voxel. Two simple approaches to approximate the normal vector are also proposed. The problem of edge artefacts that might occur in the previously proposed 3D Iterative Method Controlling the Dot Placement (IMCDP) halftoning method is discussed and a solution to reduce these artefacts is given. The results show that the proposed adaptive halftoning can combine different halftone structures on the same 3D surface with no transition artefacts between different halftone structures. It is also shown that using second-order frequency modulation (FM) halftone, in comparison to first-order FM, can result in more homogeneous appearance of 3D surfaces with undesirable structures on them. (C) 2022 Society for Imaging Science and Technology.
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.
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 appearance of dental restorations.As such, dental resin composites used in aesthetic restorative dentistry include fluorescent agents which aim to imitate 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 thickness, in reflection and in transmission modes, with the aim of improving color prediction models for these materials.
The spectral sensitivity functions of a digital image sensor determine the sensor’s color response to scene-radiated light. Knowing these spectral sensitivity functions is very important for applications that require accurate color, such as computer vision. Traditional measurements of these functions are time consuming, and require expensive lab equipment to generate narrow-band monochromatic light. Previous works have shown that sensitivity curves can be estimated using images of a color checker chart with known spectral reflectances, using either numerical optimization, or machine learning. However, previous works in the literature have not considered sensitivity functions for CFAs (color filter arrays) other than RGB, such as RCCB (Red Clear Blue) or RYYCy (Red Yellow Cyan). Non-RGB CFAs have been shown to be useful for automotive and security camera applications, especially in low light situations. We propose a machine learning method to estimate the sensitivity curves of sensors with non-RGB filters, in addition to the RGB filters addressed previously in the literature, using a single image of a color chart under unknown illumination.
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.
