Size. Mr. White (Harvey Keitel) on the right. Focus. He’s one of the two objects in focus. Lighting. Mr. White is large and in focus and Mr. Pink (Steve Buscemi) is highlighted by a shaft of light. Color. Both are black and white but the read on Mr. White’s shirt now really stands out.
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.
ACES 2.0 is the second major release of the components that make up the ACES system. The most significant change is a new suite of rendering transforms whose design was informed by collected feedback and requests from users of ACES 1. The changes aim to improve the appearance of perceived artifacts and to complete previously unfinished components of the system, resulting in a more complete, robust, and consistent product.
Highlights of the key changes in ACES 2.0 are as follows:
New output transforms, including:
A less aggressive tone scale
More intuitive controls to create custom outputs to non-standard displays
Robust gamut mapping to improve perceptual uniformity
Improved performance of the inverse transforms
Enhanced AMF specification
An updated specification for ACES Transform IDs
OpenEXR compression recommendations
Enhanced tools for generating Input Transforms and recommended procedures for characterizing prosumer cameras
Look Transform Library
Expanded documentation
Rendering Transform
The most substantial change in ACES 2.0 is a complete redesign of the rendering transform.
ACES 2.0 was built as a unified system, rather than through piecemeal additions. Different deliverable outputs “match” better and making outputs to display setups other than the provided presets is intended to be user-driven. The rendering transforms are less likely to produce undesirable artifacts “out of the box”, which means less time can be spent fixing problematic images and more time making pictures look the way you want.
Key design goals
Improve consistency of tone scale and provide an easy to use parameter to allow for outputs between preset dynamic ranges
Minimize hue skews across exposure range in a region of same hue
Unify for structural consistency across transform type
Easy to use parameters to create outputs other than the presets
Robust gamut mapping to improve harsh clipping artifacts
Fill extents of output code value cube (where appropriate and expected)
Invertible – not necessarily reversible, but Output > ACES > Output round-trip should be possible
Accomplish all of the above while maintaining an acceptable “out-of-the box” rendering
Supported by LG, Philips, Panasonic and Sony sell the OLED system TVs. OLED stands for “organic light emitting diode.” It is a fundamentally different technology from LCD, the major type of TV today. OLED is “emissive,” meaning the pixels emit their own light.
Samsung is branding its best TVs with a new acronym: “QLED” QLED (according to Samsung) stands for “quantum dot LED TV.” It is a variation of the common LED LCD, adding a quantum dot film to the LCD “sandwich.” QLED, like LCD, is, in its current form, “transmissive” and relies on an LED backlight.
OLED is the only technology capable of absolute blacks and extremely bright whites on a per-pixel basis. LCD definitely can’t do that, and even the vaunted, beloved, dearly departed plasma couldn’t do absolute blacks.
QLED, as an improvement over OLED, significantly improves the picture quality. QLED can produce an even wider range of colors than OLED, which says something about this new tech. QLED is also known to produce up to 40% higher luminance efficiency than OLED technology. Further, many tests conclude that QLED is far more efficient in terms of power consumption than its predecessor, OLED.
The goals of lighting in 3D computer graphics are more or less the same as those of real world lighting.
Lighting serves a basic function of bringing out, or pushing back the shapes of objects visible from the camera’s view.
It gives a two-dimensional image on the monitor an illusion of the third dimension-depth.
But it does not just stop there. It gives an image its personality, its character. A scene lit in different ways can give a feeling of happiness, of sorrow, of fear etc., and it can do so in dramatic or subtle ways. Along with personality and character, lighting fills a scene with emotion that is directly transmitted to the viewer.
Trying to simulate a real environment in an artificial one can be a daunting task. But even if you make your 3D rendering look absolutely photo-realistic, it doesn’t guarantee that the image carries enough emotion to elicit a “wow” from the people viewing it.
Making 3D renderings photo-realistic can be hard. Putting deep emotions in them can be even harder. However, if you plan out your lighting strategy for the mood and emotion that you want your rendering to express, you make the process easier for yourself.
Each light source can be broken down in to 4 distinct components and analyzed accordingly.
· Intensity
· Direction
· Color
· Size
The overall thrust of this writing is to produce photo-realistic images by applying good lighting techniques.
Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.
To understand what the problem is, let’s start with an example of three ways of blending green and magenta:
Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors.
Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.
Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.
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.
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.
The cone angle of the sun refers to the angular diameter of the sun as observed from Earth, which is related to the apparent size of the sun in the sky.
The angular diameter of the sun, or the cone angle of the sunlight as perceived from Earth, is approximately 0.53 degrees on average. This value can vary slightly due to the elliptical nature of Earth’s orbit around the sun, but it generally stays within a narrow range.
Here’s a more precise breakdown:
Average Angular Diameter: About 0.53 degrees (31 arcminutes)
Minimum Angular Diameter: Approximately 0.52 degrees (when Earth is at aphelion, the farthest point from the sun)
Maximum Angular Diameter: Approximately 0.54 degrees (when Earth is at perihelion, the closest point to the sun)
This angular diameter remains relatively constant throughout the day because the sun’s distance from Earth does not change significantly over a single day.
To summarize, the cone angle of the sun’s light, or its angular diameter, is typically around 0.53 degrees, regardless of the time of day.
When collecting hdri make sure the data supports basic metadata, such as:
Iso
Aperture
Exposure time or shutter time
Color temperature
Color space Exposure value (what the sensor receives of the sun intensity in lux)
7+ brackets (with 5 or 6 being the perceived balanced exposure)
In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.
The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography
In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.
The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.
HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.
“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.”
DISCLAIMER – Links and images on this website may be protected by the respective owners’ copyright. All data submitted by users through this site shall be treated as freely available to share.
