COMPOSITION
DESIGN
COLOR
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Scientists claim to have discovered ‘new colour’ no one has seen before: Olo
Read more: Scientists claim to have discovered ‘new colour’ no one has seen before: Olohttps://www.bbc.com/news/articles/clyq0n3em41o
By stimulating specific cells in the retina, the participants claim to have witnessed a blue-green colour that scientists have called “olo”, but some experts have said the existence of a new colour is “open to argument”.
The findings, published in the journal Science Advances on Friday, have been described by the study’s co-author, Prof Ren Ng from the University of California, as “remarkable”.
(A) System inputs. (i) Retina map of 103 cone cells preclassified by spectral type (7). (ii) Target visual percept (here, a video of a child, see movie S1 at 1:04). (iii) Infrared cellular-scale imaging of the retina with 60-frames-per-second rolling shutter. Fixational eye movement is visible over the three frames shown.
(B) System outputs. (iv) Real-time per-cone target activation levels to reproduce the target percept, computed by: extracting eye motion from the input video relative to the retina map; identifying the spectral type of every cone in the field of view; computing the per-cone activation the target percept would have produced. (v) Intensities of visible-wavelength 488-nm laser microdoses at each cone required to achieve its target activation level.
(C) Infrared imaging and visible-wavelength stimulation are physically accomplished in a raster scan across the retinal region using AOSLO. By modulating the visible-wavelength beam’s intensity, the laser microdoses shown in (v) are delivered. Drawing adapted with permission [Harmening and Sincich (54)].
(D) Examples of target percepts with corresponding cone activations and laser microdoses, ranging from colored squares to complex imagery. Teal-striped regions represent the color “olo” of stimulating only M cones.
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Björn Ottosson – OKlch color space
Read more: Björn Ottosson – OKlch color spaceBjörn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
- L: Lightness (the perceived brightness of the color)
- C: Chroma (the intensity or saturation of the color)
- H: Hue (the actual color, such as red, blue, green, etc.)
Also read:
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Willem Zwarthoed – Aces gamut in VFX production pdf
Read more: Willem Zwarthoed – Aces gamut in VFX production pdfhttps://www.provideocoalition.com/color-management-part-12-introducing-aces/
Local copy:
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
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Photography Basics : Spectral Sensitivity Estimation Without a Camera
Read more: Photography Basics : Spectral Sensitivity Estimation Without a Camerahttps://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
![[gamma correction test]](http://www.madore.org/~david/misc/color/gammatest.png "sRGB gamma correction test")
LIGHTING
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StudioBinder.com – Photography basics: What is Dynamic Range in Photography
Read more: StudioBinder.com – Photography basics: What is Dynamic Range in Photographyhttps://www.studiobinder.com/blog/what-is-dynamic-range-photography/
https://www.hdrsoft.com/resources/dri.html#bit-depth
The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.
For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.
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Composition – These are the basic lighting techniques you need to know for photography and film
Read more: Composition – These are the basic lighting techniques you need to know for photography and film
http://www.diyphotography.net/basic-lighting-techniques-need-know-photography-film/
Amongst the basic techniques, there’s…
1- Side lighting – Literally how it sounds, lighting a subject from the side when they’re faced toward you
2- Rembrandt lighting – Here the light is at around 45 degrees over from the front of the subject, raised and pointing down at 45 degrees
3- Back lighting – Again, how it sounds, lighting a subject from behind. This can help to add drama with silouettes
4- Rim lighting – This produces a light glowing outline around your subject
5- Key light – The main light source, and it’s not necessarily always the brightest light source
6- Fill light – This is used to fill in the shadows and provide detail that would otherwise be blackness
7- Cross lighting – Using two lights placed opposite from each other to light two subjects
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Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesis
Read more: Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesishttps://srameo.github.io/projects/le3d/
LE3D is a method for real-time HDR view synthesis from RAW images. It is particularly effective for nighttime scenes.
https://github.com/Srameo/LE3D
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Unity 3D resources
Read more: Unity 3D resources
http://answers.unity3d.com/questions/12321/how-can-i-start-learning-unity-fast-list-of-tutori.html
If you have no previous experience with Unity, start with these six video tutorials which give a quick overview of the Unity interface and some important features http://unity3d.com/support/documentation/video/
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