COMPOSITION
DESIGN
COLOR
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Image rendering bit depth
The 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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Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color picking
Read more: Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color pickinghttps://bottosson.github.io/misc/colorpicker
https://bottosson.github.io/posts/colorpicker/
https://www.smashingmagazine.com/2024/10/interview-bjorn-ottosson-creator-oklab-color-space/
One problem with sRGB is that in a gradient between blue and white, it becomes a bit purple in the middle of the transition. That’s because sRGB really isn’t created to mimic how the eye sees colors; rather, it is based on how CRT monitors work. That means it works with certain frequencies of red, green, and blue, and also the non-linear coding called gamma. It’s a miracle it works as well as it does, but it’s not connected to color perception. When using those tools, you sometimes get surprising results, like purple in the gradient.
There were also attempts to create simple models matching human perception based on XYZ, but as it turned out, it’s not possible to model all color vision that way. Perception of color is incredibly complex and depends, among other things, on whether it is dark or light in the room and the background color it is against. When you look at a photograph, it also depends on what you think the color of the light source is. The dress is a typical example of color vision being very context-dependent. It is almost impossible to model this perfectly.
I based Oklab on two other color spaces, CIECAM16 and IPT. I used the lightness and saturation prediction from CIECAM16, which is a color appearance model, as a target. I actually wanted to use the datasets used to create CIECAM16, but I couldn’t find them.
IPT was designed to have better hue uniformity. In experiments, they asked people to match light and dark colors, saturated and unsaturated colors, which resulted in a dataset for which colors, subjectively, have the same hue. IPT has a few other issues but is the basis for hue in Oklab.
In the Munsell color system, colors are described with three parameters, designed to match the perceived appearance of colors: Hue, Chroma and Value. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. The parameters are designed to be independent and each have a uniform scale. This results in a color solid with an irregular shape. Modern color spaces and models, such as CIELAB, Cam16 and Björn Ottosson own Oklab, are very similar in their construction.
By far the most used color spaces today for color picking are HSL and HSV, two representations introduced in the classic 1978 paper “Color Spaces for Computer Graphics”. HSL and HSV designed to roughly correlate with perceptual color properties while being very simple and cheap to compute.
Today HSL and HSV are most commonly used together with the sRGB color space.
One of the main advantages of HSL and HSV over the different Lab color spaces is that they map the sRGB gamut to a cylinder. This makes them easy to use since all parameters can be changed independently, without the risk of creating colors outside of the target gamut.
The main drawback on the other hand is that their properties don’t match human perception particularly well.
Reconciling these conflicting goals perfectly isn’t possible, but given that HSV and HSL don’t use anything derived from experiments relating to human perception, creating something that makes a better tradeoff does not seem unreasonable.With this new lightness estimate, we are ready to look into the construction of Okhsv and Okhsl.
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Virtual Production volumes study
Read more: Virtual Production volumes studyColor Fidelity in LED Volumes
https://theasc.com/articles/color-fidelity-in-led-volumesVirtual Production Glossary
https://vpglossary.com/What is Virtual Production – In depth analysis
https://www.leadingledtech.com/what-is-a-led-virtual-production-studio-in-depth-technical-analysis/A comparison of LED panels for use in Virtual Production:
Findings and recommendations
https://eprints.bournemouth.ac.uk/36826/1/LED_Comparison_White_Paper%281%29.pdf -
What Is The Resolution and view coverage Of The human Eye. And what distance is TV at best?
Read more: What Is The Resolution and view coverage Of The human Eye. And what distance is TV at best?https://www.discovery.com/science/mexapixels-in-human-eye
About 576 megapixels for the entire field of view.
Consider a view in front of you that is 90 degrees by 90 degrees, like looking through an open window at a scene. The number of pixels would be:
90 degrees * 60 arc-minutes/degree * 1/0.3 * 90 * 60 * 1/0.3 = 324,000,000 pixels (324 megapixels).At any one moment, you actually do not perceive that many pixels, but your eye moves around the scene to see all the detail you want. But the human eye really sees a larger field of view, close to 180 degrees. Let’s be conservative and use 120 degrees for the field of view. Then we would see:
120 * 120 * 60 * 60 / (0.3 * 0.3) = 576 megapixels.
Or.
7 megapixels for the 2 degree focus arc… + 1 megapixel for the rest.
https://clarkvision.com/articles/eye-resolution.html
Details in the post
LIGHTING
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What is the Light Field?
Read more: What is the Light Field?http://lightfield-forum.com/what-is-the-lightfield/
The light field consists of the total of all light rays in 3D space, flowing through every point and in every direction.
How to Record a Light Field
- a single, robotically controlled camera
- a rotating arc of cameras
- an array of cameras or camera modules
- a single camera or camera lens fitted with a microlens array
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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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The Color of Infinite Temperature
This is the color of something infinitely hot.
Of course you’d instantly be fried by gamma rays of arbitrarily high frequency, but this would be its spectrum in the visible range.
johncarlosbaez.wordpress.com/2022/01/16/the-color-of-infinite-temperature/
This is also the color of a typical neutron star. They’re so hot they look the same.
It’s also the color of the early Universe!This was worked out by David Madore.
The color he got is sRGB(148,177,255).
www.htmlcsscolor.com/hex/94B1FFAnd according to the experts who sip latte all day and make up names for colors, this color is called ‘Perano’.
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Photography basics: How Exposure Stops (Aperture, Shutter Speed, and ISO) Affect Your Photos – cheat sheet cards
Also see:
https://www.pixelsham.com/2018/11/22/exposure-value-measurements/
https://www.pixelsham.com/2016/03/03/f-stop-vs-t-stop/
An exposure stop is a unit measurement of Exposure as such it provides a universal linear scale to measure the increase and decrease in light, exposed to the image sensor, due to changes in shutter speed, iso and f-stop.
+-1 stop is a doubling or halving of the amount of light let in when taking a photo
1 EV (exposure value) is just another way to say one stop of exposure change.
https://www.photographymad.com/pages/view/what-is-a-stop-of-exposure-in-photography
Same applies to shutter speed, iso and aperture.
Doubling or halving your shutter speed produces an increase or decrease of 1 stop of exposure.
Doubling or halving your iso speed produces an increase or decrease of 1 stop of exposure.Because of the way f-stop numbers are calculated (ratio of focal length/lens diameter, where focal length is the distance between the lens and the sensor), an f-stop doesn’t relate to a doubling or halving of the value, but to the doubling/halving of the area coverage of a lens in relation to its focal length. And as such, to a multiplying or dividing by 1.41 (the square root of 2). For example, going from f/2.8 to f/4 is a decrease of 1 stop because 4 = 2.8 * 1.41. Changing from f/16 to f/11 is an increase of 1 stop because 11 = 16 / 1.41.
A wider aperture means that light proceeding from the foreground, subject, and background is entering at more oblique angles than the light entering less obliquely.
Consider that absolutely everything is bathed in light, therefore light bouncing off of anything is effectively omnidirectional. Your camera happens to be picking up a tiny portion of the light that’s bouncing off into infinity.
Now consider that the wider your iris/aperture, the more of that omnidirectional light you’re picking up:
When you have a very narrow iris you are eliminating a lot of oblique light. Whatever light enters, from whatever distance, enters moderately parallel as a whole. When you have a wide aperture, much more light is entering at a multitude of angles. Your lens can only focus the light from one depth – the foreground/background appear blurred because it cannot be focused on.
https://frankwhitephotography.com/index.php?id=28:what-is-a-stop-in-photography
The great thing about stops is that they give us a way to directly compare shutter speed, aperture diameter, and ISO speed. This means that we can easily swap these three components about while keeping the overall exposure the same.
http://lifehacker.com/how-aperture-shutter-speed-and-iso-affect-pictures-sh-1699204484
https://www.techradar.com/how-to/the-exposure-triangle
https://www.videoschoolonline.com/what-is-an-exposure-stop
Note. All three of these measurements (aperture, shutter, iso) have full stops, half stops and third stops, but if you look at the numbers they aren’t always consistent. For example, a one third stop between ISO100 and ISO 200 would be ISO133, yet most cameras are marked at ISO125.
Third-stops are especially important as they’re the increment that most cameras use for their settings. These are just imaginary divisions in each stop.
From a practical standpoint manufacturers only standardize the full stops, meaning that while they try and stay somewhat consistent there is some rounding up going on between the smaller numbers.Note that ND Filters directly modify the exposure triangle.
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