The 2011 Best Illusion of the Year uses motion to render color changes invisible, and so reveals a quirk in our visual systems that is new to scientists.
“It is a really beautiful effect, revealing something about how our visual system works that we didn’t know before,” said Daniel Simons, a professor at the University of Illinois, Champaign-Urbana. Simons studies visual cognition, and did not work on this illusion. Before its creation, scientists didn’t know that motion had this effect on perception, Simons said.
A viewer stares at a speck at the center of a ring of colored dots, which continuously change color. When the ring begins to rotate around the speck, the color changes appear to stop. But this is an illusion. For some reason, the motion causes our visual system to ignore the color changes. (You can, however, see the color changes if you follow the rotating circles with your eyes.)
The primary goal of physically-based rendering (PBR) is to create a simulation that accurately reproduces the imaging process of electro-magnetic spectrum radiation incident to an observer. This simulation should be indistinguishable from reality for a similar observer.
Because a camera is not sensitive to incident light the same way than a human observer, the images it captures are transformed to be colorimetric. A project might require infrared imaging simulation, a portion of the electro-magnetic spectrum that is invisible to us. Radically different observers might image the same scene but the act of observing does not change the intrinsic properties of the objects being imaged. Consequently, the physical modelling of the virtual scene should be independent of the observer.
“The list should be helpful for every material artist who work on PBR materials as it contains over 200 color values measured with PCE-RGB2 1002 Color Spectrometer device and presented in linear and sRGB (2.2) gamma space.
All color values, HUE and Saturation in this list come from measurements taken with PCE-RGB2 1002 Color Spectrometer device and are presented in linear and sRGB (2.2) gamma space (more info at the end of this video) I calculated Relative Luminance and Luminance values based on captured color using my own equation which takes color based luminance perception into consideration. Bare in mind that there is no ‘one’ color per substance as nothing in nature is even 100% uniform and any value in +/-10% range from these should be considered as correct one. Therefore this list should be always considered as a color reference for material’s albedos, not ulitimate and absolute truth.“
Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.
Artificial light sources, not unlike the diverse phases of natural light, vary considerably in their properties. As a result, some lamps render an object’s color better than others do.
The most important criterion for assessing the color-rendering ability of any lamp is its spectral power distribution curve.
Natural daylight varies too much in strength and spectral composition to be taken seriously as a lighting standard for grading and dealing colored stones. For anything to be a standard, it must be constant in its properties, which natural light is not.
For dealers in particular to make the transition from natural light to an artificial light source, that source must offer:
1- A degree of illuminance at least as strong as the common phases of natural daylight.
2- Spectral properties identical or comparable to a phase of natural daylight.
A source combining these two things makes gems appear much the same as when viewed under a given phase of natural light. From the viewpoint of many dealers, this corresponds to a naturalappearance.
The 6000° Kelvin xenon short-arc lamp appears closest to meeting the criteria for a standard light source. Besides the strong illuminance this lamp affords, its spectrum is very similar to CIE standard illuminants of similar color temperature.
Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.
The power output of a light source is measured using the unit of watts W. This is a direct measure to calculate how much power the light is going to drain from your socket and it is not relatable to the light brightness itself.
The amount of energy emitted from it per second. That energy comes out in a form of photons which we can crudely represent with rays of light coming out of the source. The higher the power the more rays emitted from the source in a unit of time.
Not all energy emitted is visible to the human eye, so we often rely on photometric measurements, which takes in account the sensitivity of human eye to different wavelenghts
Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships.
Three main types: – Image Gamma encoded in images – Display Gammas encoded in hardware and/or viewing time – System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
Bella works in spectral space, allowing effects such as BSDF wavelength dependency, diffraction, or atmosphere to be modeled far more accurately than in color space.
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