Chromaticity Matrix to Tristimulus Matrix Conversion for RGB Color Spaces – Even In the Dark

Two methods to transform primary chromaticities and white point into primary tristimulus values are examined and compared. One method appears in numerous places in the literature; we refer to this as the familiar\u27\u27 method, and provide a novel interpretation of it. The second is much less well-known and is referred to as the unfamiliar\u27\u27 method. Necessary and sufficient conditions for computing primary tristimulus values from their chromaticities are identified; in brief, the triangle in the (x,y) chromaticity diagram must have non-zero area. The computational burdens for the methods were compared; the familiar method required slightly more arithmetical operations. Two problems with the familiar method were identified: high potential for rounding error and the inability to contend with a non-luminous primary. The unfamiliar method is less prone to rounding error, and is able to contend with primaries on the alychne. It is recommended that the unfamiliar method be preferred

Relative Impact of Key Rendering Parameters on Perceived Quality of VR Imagery Captured by the Facebook Surround 360 Camera

High quality, 360 capture for Cinematic VR is a relatively new and rapidly evolving technology. The field demands very high quality, distortion- free 360 capture which is not possible with cameras that depend on fish- eye lenses for capturing a 360 field of view. The Facebook Surround 360 Camera, one of the few “players” in this space, is an open-source license design that Facebook has released for anyone that chooses to build it from off-the-shelf components and generate 8K stereo output using open-source licensed rendering software. However, the components are expensive and the system itself is extremely demanding in terms of computer hardware and software. Because of this, there have been very few implementations of this design and virtually no real deployment in the field. We have implemented the system, based on Facebook’s design, and have been testing and deploying it in various situations; even generating short video clips. We have discovered in our recent experience that high quality, 360 capture comes with its own set of new challenges. As an example, even the most fundamental tools of photography like “exposure” become difficult because one is always faced with ultra-high dynamic range scenes (one camera is pointing directly at the sun and the others may be pointing to a dark shadow). The conventional imaging pipeline is further complicated by the fact that the stitching software has different effects on various as- pects of the calibration or pipeline optimization. Most of our focus to date has been on optimizing the imaging pipeline and improving the qual- ity of the output for viewing in an Oculus Rift headset. We designed a controlled experiment to study 5 key parameters in the rendering pipeline– black level, neutral balance, color correction matrix (CCM), geometric calibration and vignetting. By varying all of these parameters in a combinatorial manner, we were able to assess the relative impact of these parameters on the perceived image quality of the output. Our results thus far indicate that the output image quality is greatly influenced by the black level of the individual cameras (the Facebook cam- era comprised of 17 cameras whose output need to be stitched to obtain a 360 view). Neutral balance is least sensitive. We are most confused about the results we obtain from accurately calculating and applying the CCM for each individual camera. We obtained improved results by using the average of the matrices for all cameras. Future work includes evaluating the effects of geometric calibration and vignetting on quality

Modeling the Color of Multi-Colored Halftones

ABSTRACT A mathematical model which provides the relationship between the dot areas of a combination of halftone patterns and the color produced by such multi-colored halftone patterns is disclosed. Such a model has application in calibration of digital color systems, digital picture exchange, and the simulation of "spot color" using process color techniques. The new model is an extension of the Spectral Yule-Nielsen model described by this author at a previous TAGA conference. It provides greater accuracy than the Neugebauer model, either with or without the Yule-Nielsen correction. Experimental verification of the Demichel dot overlap model is presented. The Demichel model was found to be accurate for halftone patterns superimposed at a 30 degree orientation. The new model provides an acceptable level of accuracy for many applications. In an experimental evaluation, many predictions of the new model were sufficiently close to measured color so as to be indistinguishable to a human observer

Assessing the Ability of Simulated Laboratory Scenes to Predict the Image Quality Performance of HDR Captures (and Rendering) of Exterior Scenes Using Mobile Phone Cameras

With the advent of computational photography, most cellphones include High Dynamic Range (HDR) modes or “apps” that capture and render high contrast scenes in-camera using techniques such as multiple exposures and subsequent “addition” of those exposures to render a properly exposed image. The results from different cameras vary. Testing the image quality of different cameras involves field-testing under dynamic lighting conditions that may involve moving objects. Such testing often becomes a cumbersome and time-consuming task. It would be more efficient to conduct such testing in a controlled, laboratory environment. This study investigates the feasibility of such testing. Natural exterior scenes, at day and night, some of which include “motion”, were captured with a range of cellphone cameras using their native HDR modes. The luminance ratios of these scenes were accurately measured using various spectro-radiometers and luminance meters. Artificial scenes, which include characteristics of the natural exterior scenes and have similar luminance ratios, were created in a laboratory environment. These simulated scenes were captured using the same modes as the natural exterior scenes. A subjective image quality evaluation was conducted using some 20 observers to establish an observer preference scale separately for each scene. For each natural exterior scene, the correlation coefficients between its preference scale and the preference scale obtained for each laboratory scene were calculated, and the laboratory scene with the highest correlation was identified. It was determined that while it was difficult to accurately quantify the actual dynamic range of a natural exterior scene, especially at night, we could still simulate the luminance ratios of a wide range of natural exterior HDR scenes, from 266:1 to 15120:1, within a laboratory environment. Preliminary results of the subjective study indicated that reasonably good correlation (0.8 or higher on average) was obtained between the natural exterior and laboratory simulated scenes. However, such correlations were determined to be specific to the type of scene studied. The scope of this study needs to be narrowed. Another consideration, how moving objects in the scene would affect the results, needs further investigation

Inertial particle clustering due to turbulence in an air jet

ABSTRACT: Explosive volcanic eruptions create turbulent plumes of fine ash particles. When these particles collide in the presence of moisture and electrostatic fields they combine into larger aggregates, which can significantly change the atmospheric residence time of the airborne cloud. Previous studies have suggested that turbulence may lead to preferential concentration—also known as clustering—of particles within the flow, increasing the likelihood of collisions and aggregation. Few experimental studies have quantified these processes for volcanic plumes. This behavior was investigated using a particle-laden air jet. By systematically varying the exit speed and the size, density, and concentration of particles, flows were produced with Reynolds numbers of 4940 to 19300, Stokes numbers of 1.0 to 17.4 (based on the convective scale), and particle mass loadings of 0.3 to 3.9%. Specific emphasis is placed on two Stokes numbers of 1.9 and 17.4, which differ by nearly an order of magnitude. Particle image velocimetry was employed to measure the velocity distribution within a two-dimensional rectangular region along the jet centerline in each experiment. Voronoï decomposition was used to quantify the extent of preferential concentration by measuring the distribution of cell sizes around each individual particle. Results show that particles exhibit clustering behavior when Stokes numbers are close to 1. We also measured the radial distribution functions (RDFs) to quantify the likelihood of particle collisions. At low Stokes number, the RDF magnitude was significantly higher, which corresponds to increased collision frequency in the particle-laden jet. Computational analysis finds that increasing the RDF by a factor of 20 results in a doubling of peak aggregate size. These findings demonstrate that preferential concentration due to turbulent structures could have important effects on collision frequencies, ash aggregation, and electrification in volcanic plumes

Lagrangian Diffusion Properties of a Free Shear Turbulent Jet

A Lagrangian experimental study of an axisymmetric turbulent water jet is performed to investigate the highly anisotropic and inhomogeneous flow field. Measurements are conducted within a Lagrangian exploration module, an icosahedron apparatus, to facilitate optical access of three cameras. Stereoscopic particle tracking velocimetry results in three-component tracks of position, velocity and acceleration of the tracer particles within the vertically oriented jet with a Taylor-based Reynolds number Reλ≃230. Analysis is performed at seven locations from 15 diameters up to 45 diameters downstream. Eulerian analysis is first carried out to obtain critical parameters of the jet and relevant scales, namely the Kolmogorov and large (integral) scales as well as the energy dissipation rate. Lagrangian statistical analysis is then performed on velocity components stationarised following methods inspired by Batchelor (J. Fluid Mech., vol. 3, 1957, pp. 67–80), which aim to extend stationary Lagrangian theory of turbulent diffusion by Taylor to the case of self-similar flows. The evolution of typical Lagrangian scaling parameters as a function of the developing jet is explored and results show validation of the proposed stationarisation. The universal scaling constant C0 (for the Lagrangian second-order structure function), as well as Eulerian and Lagrangian integral time scales, are discussed in this context. Constant C0 is found to converge to a constant value (of the order of C0=3) within 30 diameters downstream of the nozzle. Finally, the occurrence of finite particle size effects is investigated through consideration of acceleration-dependent quantities

Qualitative Behaviour Assessment of dogs in the shelter and home environment and relationship with quantitative behaviour assessment and physiological responses

Qualitative Behaviour Assessment (QBA) was utilised to examine the behavioural expression of dogs in different housing environments and the results were compared to measurements of quantitative behaviour and physiology. Firstly, quantitative behavioural and physiological differences were investigated between dogs in 3 housing environments (short-term shelter confinement, ≤ 4 days, n = 10; long-term shelter confinement, > 30 days, n = 9; and domestic living situations, n = 10). Each dog’s behaviour was recorded over a 4 h period using an ethogram consisting of 21 behaviour categories. Dogs in both short (SD) and long (LD) term confinement displayed higher frequencies of paw-lifting (P < 0.001), displacement behaviour (digging and/or drinking P < 0.01), vocalisation (P < 0.05) and locomotory activity (P < 0.001) compared to dogs maintained as family pets (PD). Salivary cortisol concentrations did not differ amongst groups (H = 0.55, P = 0.76). Secondly, quantitative behaviour and QBA were combined to investigate differences among these same 29 dogs when filmed for 1 min in both their Home Environment and a standardised Novel Environment. QBA of these video clips was made by 10 observers utilising Free-Choice-Profiling methodology. Generalised Procrustes Analysis was used to calculate a consensus profile and three main dimensions of dog expression in both Environments. The observers repeated dog scores on these dimensions with high accuracy (P < 0.001). Observers perceived dogs as more ‘relaxed/content’ in the Home Environment (H = 17.86, P < 0.0001), and more ‘calm/relaxed’ in the Novel Environment (H = 13.58, P < 0.001), than SD and LD dogs. In the Novel Environment, LD dogs were perceived as more ‘inquisitive/curious’ (H = 5.97, P < 0.05), and SD dogs as more ‘curious/cautious’ (H = 6.82, P < 0.05), than the other groups. Quantitative assessment of the 1 min Home and Novel Environment video clips were analysed using Principle Component Analysis (PCA), generating two main factors explaining 88% and 76% of the variation respectively. PCA factor 1 (‘rest’) and QBA Dimension 1 (‘relaxed/content’) correlated (P < 0.0001) in the Home Environment’. In the Novel Environment PCA factor 1 (‘stand’, ‘sniff’) correlated with QBA Dimension 1 (‘clam/relaxed’) and PCA factor 2 (‘sniff’, ‘walk’) correlated with QBA Dimension 2 (‘curious/inquisitive’). There was no correlation between QBA dimensions and cortisol concentrations. In sum, these results indicate that a combined quantitative/qualitative assessment facilitates the interpretation of behavioural variances resulting from housing differences and supports utilising QBA for the assessment of dog behavioural expression