COMPOSITION
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Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental process
Read more: Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental processhttps://www.chrbutler.com/understanding-the-eye-mind-connection
The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies.
This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes.
Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles:
- Attention and Engagement
- Visual Hierarchy
- Cognitive Load Management
- Context and Meaning
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Composition – How to make a slow motion shot
Read more: Composition – How to make a slow motion shothttp://www.slomovideo.com/slowhow.html
Slow motion cinematography is the result of playing back frames for a longer duration than they were exposed. For example, if you expose 240 frames of film in one second, then play them back at 24 fps, the resulting movie is 10 times longer (slower) than the original filmed event….
DESIGN
COLOR
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Capturing textures albedo
Read more: Capturing textures albedoBuilding a Portable PBR Texture Scanner by Stephane Lb
http://rtgfx.com/pbr-texture-scanner/
How To Split Specular And Diffuse In Real Images, by John Hable
http://filmicworlds.com/blog/how-to-split-specular-and-diffuse-in-real-images/Capturing albedo using a Spectralon
https://www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdfReal_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
Spectralon is a teflon-based pressed powderthat comes closest to being a pure Lambertian diffuse material that reflects 100% of all light. If we take an HDR photograph of the Spectralon alongside the material to be measured, we can derive thediffuse albedo of that material.
The process to capture diffuse reflectance is very similar to the one outlined by Hable.
1. We put a linear polarizing filter in front of the camera lens and a second linear polarizing filterin front of a modeling light or a flash such that the two filters are oriented perpendicular to eachother, i.e. cross polarized.
2. We place Spectralon close to and parallel with the material we are capturing and take brack-eted shots of the setup7. Typically, we’ll take nine photographs, from -4EV to +4EV in 1EVincrements.
3. We convert the bracketed shots to a linear HDR image. We found that many HDR packagesdo not produce an HDR image in which the pixel values are linear. PTGui is an example of apackage which does generate a linear HDR image. At this point, because of the cross polarization,the image is one of surface diffuse response.
4. We open the file in Photoshop and normalize the image by color picking the Spectralon, filling anew layer with that color and setting that layer to “Divide”. This sets the Spectralon to 1 in theimage. All other color values are relative to this so we can consider them as diffuse albedo.
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A Brief History of Color in Art
Read more: A Brief History of Color in Artwww.artsy.net/article/the-art-genome-project-a-brief-history-of-color-in-art
Of all the pigments that have been banned over the centuries, the color most missed by painters is likely Lead White.
This hue could capture and reflect a gleam of light like no other, though its production was anything but glamorous. The 17th-century Dutch method for manufacturing the pigment involved layering cow and horse manure over lead and vinegar. After three months in a sealed room, these materials would combine to create flakes of pure white. While scientists in the late 19th century identified lead as poisonous, it wasn’t until 1978 that the United States banned the production of lead white paint.
More reading:
www.canva.com/learn/color-meanings/https://www.infogrades.com/history-events-infographics/bizarre-history-of-colors/
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What is a Gamut or Color Space and why do I need to know about CIE
Read more: What is a Gamut or Color Space and why do I need to know about CIE
http://www.xdcam-user.com/2014/05/what-is-a-gamut-or-color-space-and-why-do-i-need-to-know-about-it/
In video terms gamut is normally related to as the full range of colours and brightness that can be either captured or displayed.
Generally speaking all color gamuts recommendations are trying to define a reasonable level of color representation based on available technology and hardware. REC-601 represents the old TVs. REC-709 is currently the most distributed solution. P3 is mainly available in movie theaters and is now being adopted in some of the best new 4K HDR TVs. Rec2020 (a wider space than P3 that improves on visibke color representation) and ACES (the full coverage of visible color) are other common standards which see major hardware development these days.
To compare and visualize different solution (across video and printing solutions), most developers use the CIE color model chart as a reference.
The CIE color model is a color space model created by the International Commission on Illumination known as the Commission Internationale de l’Elcairage (CIE) in 1931. It is also known as the CIE XYZ color space or the CIE 1931 XYZ color space.
This chart represents the first defined quantitative link between distributions of wavelengths in the electromagnetic visible spectrum, and physiologically perceived colors in human color vision. Or basically, the range of color a typical human eye can perceive through visible light.
Note that while the human perception is quite wide, and generally speaking biased towards greens (we are apes after all), the amount of colors available through nature, generated through light reflection, tend to be a much smaller section. This is defined by the Pointer’s Chart.
In short. Color gamut is a representation of color coverage, used to describe data stored in images against available hardware and viewer technologies.
Camera color encoding from
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
CIE 1976
http://bernardsmith.eu/computatrum/scan_and_restore_archive_and_print/scanning/
https://store.yujiintl.com/blogs/high-cri-led/understanding-cie1931-and-cie-1976
The CIE 1931 standard has been replaced by a CIE 1976 standard. Below we can see the significance of this.
People have observed that the biggest issue with CIE 1931 is the lack of uniformity with chromaticity, the three dimension color space in rectangular coordinates is not visually uniformed.
The CIE 1976 (also called CIELUV) was created by the CIE in 1976. It was put forward in an attempt to provide a more uniform color spacing than CIE 1931 for colors at approximately the same luminance
The CIE 1976 standard colour space is more linear and variations in perceived colour between different people has also been reduced. The disproportionately large green-turquoise area in CIE 1931, which cannot be generated with existing computer screens, has been reduced.
If we move from CIE 1931 to the CIE 1976 standard colour space we can see that the improvements made in the gamut for the “new” iPad screen (as compared to the “old” iPad 2) are more evident in the CIE 1976 colour space than in the CIE 1931 colour space, particularly in the blues from aqua to deep blue.
https://dot-color.com/2012/08/14/color-space-confusion/
Despite its age, CIE 1931, named for the year of its adoption, remains a well-worn and familiar shorthand throughout the display industry. CIE 1931 is the primary language of customers. When a customer says that their current display “can do 72% of NTSC,” they implicitly mean 72% of NTSC 1953 color gamut as mapped against CIE 1931.
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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.
LIGHTING
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IES Light Profiles and editing software
Read more: IES Light Profiles and editing softwarehttp://www.derekjenson.com/3d-blog/ies-light-profiles
https://ieslibrary.com/en/browse#ies
https://leomoon.com/store/shaders/ies-lights-pack
https://docs.arnoldrenderer.com/display/a5afmug/ai+photometric+light
IES profiles are useful for creating life-like lighting, as they can represent the physical distribution of light from any light source.
The IES format was created by the Illumination Engineering Society, and most lighting manufacturers provide IES profile for the lights they manufacture.
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About green screens
Read more: About green screenshackaday.com/2015/02/07/how-green-screen-worked-before-computers/
www.newtek.com/blog/tips/best-green-screen-materials/
www.chromawall.com/blog//chroma-key-green
Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS).
Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production:
Green Screen as RGB Color Value: 0, 177, 64
Green Screen as CMYK Color Value: 81, 0, 92, 0
Green Screen as Hex Color Value: #00b140
Green Screen as Websafe Color Value: #009933Chroma Key Green is reasonably close to an 18% gray reflectance.
Illuminate your green screen with an uniform source with less than 2/3 EV variation.
The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting. -
Convert between light exposure and intensity
Read more: Convert between light exposure and intensityimport math,sys def Exposure2Intensity(exposure): exp = float(exposure) result = math.pow(2,exp) print(result) Exposure2Intensity(0) def Intensity2Exposure(intensity): inarg = float(intensity) if inarg == 0: print("Exposure of zero intensity is undefined.") return if inarg < 1e-323: inarg = max(inarg, 1e-323) print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)") result = math.log(inarg, 2) print(result) Intensity2Exposure(0.1)
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