COMPOSITION
DESIGN
COLOR
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Photography basics: Why Use a (MacBeth) Color Chart?
Read more: Photography basics: Why Use a (MacBeth) Color Chart?Start here: https://www.pixelsham.com/2013/05/09/gretagmacbeth-color-checker-numeric-values/
https://www.studiobinder.com/blog/what-is-a-color-checker-tool/
In LightRoom
in Final Cut
in Nuke
Note: In Foundry’s Nuke, the software will map 18% gray to whatever your center f/stop is set to in the viewer settings (f/8 by default… change that to EV by following the instructions below).
You can experiment with this by attaching an Exposure node to a Constant set to 0.18, setting your viewer read-out to Spotmeter, and adjusting the stops in the node up and down. You will see that a full stop up or down will give you the respective next value on the aperture scale (f8, f11, f16 etc.).One stop doubles or halves the amount or light that hits the filmback/ccd, so everything works in powers of 2.
So starting with 0.18 in your constant, you will see that raising it by a stop will give you .36 as a floating point number (in linear space), while your f/stop will be f/11 and so on.If you set your center stop to 0 (see below) you will get a relative readout in EVs, where EV 0 again equals 18% constant gray.
In other words. Setting the center f-stop to 0 means that in a neutral plate, the middle gray in the macbeth chart will equal to exposure value 0. EV 0 corresponds to an exposure time of 1 sec and an aperture of f/1.0.
This will set the sun usually around EV12-17 and the sky EV1-4 , depending on cloud coverage.
To switch Foundry’s Nuke’s SpotMeter to return the EV of an image, click on the main viewport, and then press s, this opens the viewer’s properties. Now set the center f-stop to 0 in there. And the SpotMeter in the viewport will change from aperture and fstops to EV.
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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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Black Body color aka the Planckian Locus curve for white point eye perception
Read more: Black Body color aka the Planckian Locus curve for white point eye perceptionhttp://en.wikipedia.org/wiki/Black-body_radiation
Black-body radiation is the type of electromagnetic radiation within or surrounding a body in thermodynamic equilibrium with its environment, or emitted by a black body (an opaque and non-reflective body) held at constant, uniform temperature. The radiation has a specific spectrum and intensity that depends only on the temperature of the body.
A black-body at room temperature appears black, as most of the energy it radiates is infra-red and cannot be perceived by the human eye. At higher temperatures, black bodies glow with increasing intensity and colors that range from dull red to blindingly brilliant blue-white as the temperature increases.
The Black Body Ultraviolet Catastrophe Experiment
In photography, color temperature describes the spectrum of light which is radiated from a “blackbody” with that surface temperature. A blackbody is an object which absorbs all incident light — neither reflecting it nor allowing it to pass through.
The Sun closely approximates a black-body radiator. Another rough analogue of blackbody radiation in our day to day experience might be in heating a metal or stone: these are said to become “red hot” when they attain one temperature, and then “white hot” for even higher temperatures. Similarly, black bodies at different temperatures also have varying color temperatures of “white light.”
Despite its name, light which may appear white does not necessarily contain an even distribution of colors across the visible spectrum.
Although planets and stars are neither in thermal equilibrium with their surroundings nor perfect black bodies, black-body radiation is used as a first approximation for the energy they emit. Black holes are near-perfect black bodies, and it is believed that they emit black-body radiation (called Hawking radiation), with a temperature that depends on the mass of the hole.
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Image rendering bit depth
Read more: Image rendering bit depthThe terms 8-bit, 16-bit, 16-bit float, and 32-bit refer to different data formats used to store and represent image information, as bits per pixel.
https://en.wikipedia.org/wiki/Color_depth
In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.
When referring to a pixel, the concept can be defined as bits per pixel (bpp).
When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.
Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.
Here’s a simple explanation of each.
8-bit images (i.e. 24 bits per pixel for a color image) are considered Low Dynamic Range.
They can store around 5 stops of light and each pixel carry a value from 0 (black) to 255 (white).
As a comparison, DSLR cameras can capture ~12-15 stops of light and they use RAW files to store the information.16-bit: This format is commonly referred to as “half-precision.” It uses 16 bits of data to represent color values for each pixel. With 16 bits, you can have 65,536 discrete levels of color, allowing for relatively high precision and smooth gradients. However, it has a limited dynamic range, meaning it cannot accurately represent extremely bright or dark values. It is commonly used for regular images and textures.
16-bit float: This format is an extension of the 16-bit format but uses floating-point numbers instead of fixed integers. Floating-point numbers allow for more precise calculations and a larger dynamic range. In this case, the 16 bits are used to store both the color value and the exponent, which controls the range of values that can be represented. The 16-bit float format provides better accuracy and a wider dynamic range than regular 16-bit, making it useful for high-dynamic-range imaging (HDRI) and computations that require more precision.
32-bit: (i.e. 96 bits per pixel for a color image) are considered High Dynamic Range. This format, also known as “full-precision” or “float,” uses 32 bits to represent color values and offers the highest precision and dynamic range among the three options. With 32 bits, you have a significantly larger number of discrete levels, allowing for extremely accurate color representation, smooth gradients, and a wide range of brightness values. It is commonly used for professional rendering, visual effects, and scientific applications where maximum precision is required.
Bits and HDR coverage
High Dynamic Range (HDR) images are designed to capture a wide range of luminance values, from the darkest shadows to the brightest highlights, in order to reproduce a scene with more accuracy and detail. The bit depth of an image refers to the number of bits used to represent each pixel’s color information. When comparing 32-bit float and 16-bit float HDR images, the drop in accuracy primarily relates to the precision of the color information.
A 32-bit float HDR image offers a higher level of precision compared to a 16-bit float HDR image. In a 32-bit float format, each color channel (red, green, and blue) is represented by 32 bits, allowing for a larger range of values to be stored. This increased precision enables the image to retain more details and subtleties in color and luminance.
On the other hand, a 16-bit float HDR image utilizes 16 bits per color channel, resulting in a reduced range of values that can be represented. This lower precision leads to a loss of fine details and color nuances, especially in highly contrasted areas of the image where there are significant differences in luminance.
The drop in accuracy between 32-bit and 16-bit float HDR images becomes more noticeable as the exposure range of the scene increases. Exposure range refers to the span between the darkest and brightest areas of an image. In scenes with a limited exposure range, where the luminance differences are relatively small, the loss of accuracy may not be as prominent or perceptible. These images usually are around 8-10 exposure levels.
However, in scenes with a wide exposure range, such as a landscape with deep shadows and bright highlights, the reduced precision of a 16-bit float HDR image can result in visible artifacts like color banding, posterization, and loss of detail in both shadows and highlights. The image may exhibit abrupt transitions between tones or colors, which can appear unnatural and less realistic.
To provide a rough estimate, it is often observed that exposure values beyond approximately ±6 to ±8 stops from the middle gray (18% reflectance) may be more prone to accuracy issues in a 16-bit float format. This range may vary depending on the specific implementation and encoding scheme used.
To summarize, the drop in accuracy between 32-bit and 16-bit float HDR images is mainly related to the reduced precision of color information. This decrease in precision becomes more apparent in scenes with a wide exposure range, affecting the representation of fine details and leading to visible artifacts in the image.
In practice, this means that exposure values beyond a certain range will experience a loss of accuracy and detail when stored in a 16-bit float format. The exact range at which this loss occurs depends on the encoding scheme and the specific implementation. However, in general, extremely bright or extremely dark values that fall outside the representable range may be subject to quantization errors, resulting in loss of detail, banding, or other artifacts.
HDRs used for lighting purposes are usually slightly convolved to improve on sampling speed and removing specular artefacts. To that extent, 16 bit float HDRIs tend to me most used in CG cycles.
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Paul Debevec, Chloe LeGendre, Lukas Lepicovsky – Jointly Optimizing Color Rendition and In-Camera Backgrounds in an RGB Virtual Production Stage
Read more: Paul Debevec, Chloe LeGendre, Lukas Lepicovsky – Jointly Optimizing Color Rendition and In-Camera Backgrounds in an RGB Virtual Production Stagehttps://arxiv.org/pdf/2205.12403.pdf
RGB LEDs vs RGBWP (RGB + lime + phospor converted amber) LEDs
Local copy:
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GretagMacbeth Color Checker Numeric Values and Middle Gray
Read more: GretagMacbeth Color Checker Numeric Values and Middle GrayThe human eye perceives half scene brightness not as the linear 50% of the present energy (linear nature values) but as 18% of the overall brightness. We are biased to perceive more information in the dark and contrast areas. A Macbeth chart helps with calibrating back into a photographic capture into this “human perspective” of the world.
https://en.wikipedia.org/wiki/Middle_gray
In photography, painting, and other visual arts, middle gray or middle grey is a tone that is perceptually about halfway between black and white on a lightness scale in photography and printing, it is typically defined as 18% reflectance in visible light
Light meters, cameras, and pictures are often calibrated using an 18% gray card[4][5][6] or a color reference card such as a ColorChecker. On the assumption that 18% is similar to the average reflectance of a scene, a grey card can be used to estimate the required exposure of the film.
https://en.wikipedia.org/wiki/ColorChecker
The exposure meter in the camera does not know whether the subject itself is bright or not. It simply measures the amount of light that comes in, and makes a guess based on that. The camera will aim for 18% gray independently, meaning if you take a photo of an entirely white surface, and an entirely black surface you should get two identical images which both are gray (at least in theory). Thus enters the Macbeth chart.
<!–more–>
Note that Chroma Key Green is reasonably close to an 18% gray reflectance.
http://www.rags-int-inc.com/PhotoTechStuff/MacbethTarget/
No Camera Data 
https://upload.wikimedia.org/wikipedia/commons/b/b4/CIE1931xy_ColorChecker_SMIL.svg
RGB coordinates of the Macbeth ColorChecker
https://pdfs.semanticscholar.org/0e03/251ad1e6d3c3fb9cb0b1f9754351a959e065.pdf
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What light is best to illuminate gems for resale
Read more: What light is best to illuminate gems for resalewww.palagems.com/gem-lighting2
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.
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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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How are Energy and Matter the Same?
Read more: How are Energy and Matter the Same?www.turnerpublishing.com/blog/detail/everything-is-energy-everything-is-one-everything-is-possible/
www.universetoday.com/116615/how-are-energy-and-matter-the-same/
As Einstein showed us, light and matter and just aspects of the same thing. Matter is just frozen light. And light is matter on the move. Albert Einstein’s most famous equation says that energy and matter are two sides of the same coin. How does one become the other?
Relativity requires that the faster an object moves, the more mass it appears to have. This means that somehow part of the energy of the car’s motion appears to transform into mass. Hence the origin of Einstein’s equation. How does that happen? We don’t really know. We only know that it does.
Matter is 99.999999999999 percent empty space. Not only do the atom and solid matter consist mainly of empty space, it is the same in outer space
The quantum theory researchers discovered the answer: Not only do particles consist of energy, but so does the space between. This is the so-called zero-point energy. Therefore it is true: Everything consists of energy.
Energy is the basis of material reality. Every type of particle is conceived of as a quantum vibration in a field: Electrons are vibrations in electron fields, protons vibrate in a proton field, and so on. Everything is energy, and everything is connected to everything else through fields.
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Neural Microfacet Fields for Inverse Rendering
Read more: Neural Microfacet Fields for Inverse Renderinghttps://half-potato.gitlab.io/posts/nmf/
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NVidia DiffusionRenderer – Neural Inverse and Forward Rendering with Video Diffusion Models. How NVIDIA reimagined relighting
Read more: NVidia DiffusionRenderer – Neural Inverse and Forward Rendering with Video Diffusion Models. How NVIDIA reimagined relightinghttps://www.fxguide.com/quicktakes/diffusing-reality-how-nvidia-reimagined-relighting/
https://research.nvidia.com/labs/toronto-ai/DiffusionRenderer/
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HDRI Median Cut plugin
Read more: HDRI Median Cut pluginwww.hdrlabs.com/picturenaut/plugins.html
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.
Here is an openCV example:
# bottom left coordinates = 0,0 import numpy as np import cv2 # Load the HDR or EXR image image = cv2.imread('your_image_path.exr', cv2.IMREAD_UNCHANGED) # Load as-is without modification # Calculate the luminance from the HDR channels (assuming RGB format) luminance = np.dot(image[..., :3], [0.299, 0.587, 0.114]) # Set a threshold value based on estimated EV threshold_value = 2.4 # Estimated threshold value based on 4.8 EV # Apply the threshold to identify bright areas # Theluminancearray contains the calculated luminance values for each pixel in the image. # Thethreshold_valueis a user-defined value that represents a cutoff point, separating "bright" and "dark" areas in terms of perceived luminance.thresholded = (luminance > threshold_value) * 255 # Convert the thresholded image to uint8 for contour detection thresholded = thresholded.astype(np.uint8) # Find contours of the bright areas contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Create a list to store the bounding boxes of bright areas bright_areas = [] # Iterate through contours and extract bounding boxes for contour in contours: x, y, w, h = cv2.boundingRect(contour) # Adjust y-coordinate based on bottom-left origin y_bottom_left_origin = image.shape[0] - (y + h) bright_areas.append((x, y_bottom_left_origin, x + w, y_bottom_left_origin + h)) # Store as (x1, y1, x2, y2) # Print the identified bright areas print("Bright Areas (x1, y1, x2, y2):") for area in bright_areas: print(area)More details
Luminance and Exposure in an EXR Image:
- An EXR (Extended Dynamic Range) image format is often used to store high dynamic range (HDR) images that contain a wide range of luminance values, capturing both dark and bright areas.
- Luminance refers to the perceived brightness of a pixel in an image. In an RGB image, luminance is often calculated using a weighted sum of the red, green, and blue channels, where different weights are assigned to each channel to account for human perception.
- In an EXR image, the pixel values can represent radiometrically accurate scene values, including actual radiance or irradiance levels. These values are directly related to the amount of light emitted or reflected by objects in the scene.
The luminance line is calculating the luminance of each pixel in the image using a weighted sum of the red, green, and blue channels. The three float values [0.299, 0.587, 0.114] are the weights used to perform this calculation.
These weights are based on the concept of luminosity, which aims to approximate the perceived brightness of a color by taking into account the human eye’s sensitivity to different colors. The values are often derived from the NTSC (National Television System Committee) standard, which is used in various color image processing operations.
Here’s the breakdown of the float values:
- 0.299: Weight for the red channel.
- 0.587: Weight for the green channel.
- 0.114: Weight for the blue channel.
The weighted sum of these channels helps create a grayscale image where the pixel values represent the perceived brightness. This technique is often used when converting a color image to grayscale or when calculating luminance for certain operations, as it takes into account the human eye’s sensitivity to different colors.
For the threshold, remember that the exact relationship between EV values and pixel values can depend on the tone-mapping or normalization applied to the HDR image, as well as the dynamic range of the image itself.
To establish a relationship between exposure and the threshold value, you can consider the relationship between linear and logarithmic scales:
- Linear and Logarithmic Scales:
- Exposure values in an EXR image are often represented in logarithmic scales, such as EV (exposure value). Each increment in EV represents a doubling or halving of the amount of light captured.
- Threshold values for luminance thresholding are usually linear, representing an actual luminance level.
- Conversion Between Scales:
- To establish a mathematical relationship, you need to convert between the logarithmic exposure scale and the linear threshold scale.
- One common method is to use a power function. For instance, you can use a power function to convert EV to a linear intensity value.
threshold_value = base_value * (2 ** EV)Here,
EVis the exposure value,base_valueis a scaling factor that determines the relationship between EV and threshold_value, and2 ** EVis used to convert the logarithmic EV to a linear intensity value. - Choosing the Base Value:
- The
base_valuefactor should be determined based on the dynamic range of your EXR image and the specific luminance values you are dealing with. - You may need to experiment with different values of
base_valueto achieve the desired separation of bright areas from the rest of the image.
- The
Let’s say you have an EXR image with a dynamic range of 12 EV, which is a common range for many high dynamic range images. In this case, you want to set a threshold value that corresponds to a certain number of EV above the middle gray level (which is often considered to be around 0.18).
Here’s an example of how you might determine a
base_valueto achieve this:# Define the dynamic range of the image in EV dynamic_range = 12 # Choose the desired number of EV above middle gray for thresholding desired_ev_above_middle_gray = 2 # Calculate the threshold value based on the desired EV above middle gray threshold_value = 0.18 * (2 ** (desired_ev_above_middle_gray / dynamic_range)) print("Threshold Value:", threshold_value) -
Tracing Spherical harmonics and how Weta used them in production
Read more: Tracing Spherical harmonics and how Weta used them in productionA way to approximate complex lighting in ultra realistic renders.
All SH lighting techniques involve replacing parts of standard lighting equations with spherical functions that have been projected into frequency space using the spherical harmonics as a basis.
http://www.cs.columbia.edu/~cs4162/slides/spherical-harmonic-lighting.pdf
Spherical harmonics as used at Weta Digital
https://www.fxguide.com/fxfeatured/the-science-of-spherical-harmonics-at-weta-digital/
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