COMPOSITION
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Composition – cinematography Cheat Sheet
Read more: Composition – cinematography Cheat Sheet
Where is our eye attracted first? Why?
Size. Focus. Lighting. Color.
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.
What type of lighting?-> High key lighting.
Features bright, even illumination and few conspicuous shadows. This lighting key is often used in musicals and comedies.Low key lighting
Features diffused shadows and atmospheric pools of light. This lighting key is often used in mysteries and thrillers.High contrast lighting
Features harsh shafts of lights and dramatic streaks of blackness. This type of lighting is often used in tragedies and melodramas.What type of shot?
Extreme long shot
Taken from a great distance, showing much of the locale. Ifpeople are included in these shots, they usually appear as mere specks-> Long shot
Corresponds to the space between the audience and the stage in a live theater. The long shots show the characters and some of the locale.Full shot
Range with just enough space to contain the human body in full. The full shot shows the character and a minimal amount of the locale.Medium shot
Shows the human figure from the knees or waist up.Close-Up
Concentrates on a relatively small object and show very little if any locale.Extreme close-up
Focuses on an unnaturally small portion of an object, giving that part great detail and symbolic significance.What angle?
Bird’s-eye view.
The shot is photographed directly from above. This type of shot can be disorienting, and the people photographed seem insignificant.High angle.
This angle reduces the size of the objects photographed. A person photographed from this angle seems harmless and insignificant, but to a lesser extent than with the bird’s-eye view.-> Eye-level shot.
The clearest view of an object, but seldom intrinsically dramatic, because it tends to be the norm.Low angle.
This angle increases high and a sense of verticality, heightening the importance of the object photographed. A person shot from this angle is given a sense of power and respect.Oblique angle.
For this angle, the camera is tilted laterally, giving the image a slanted appearance. Oblique angles suggest tension, transition, a impending movement. They are also called canted or dutch angles.What is the dominant color?
The use of color in this shot is symbolic. The scene is set in warehouse. Both the set and characters are blues, blacks and whites.
This was intentional allowing for the scenes and shots with blood to have a great level of contrast.
What is the Lens/Filter/Stock?
Telephoto lens.
A lens that draws objects closer but also diminishes the illusion of depth.Wide-angle lens.
A lens that takes in a broad area and increases the illusion of depth but sometimes distorts the edges of the image.Fast film stock.
Highly sensitive to light, it can register an image with little illumination. However, the final product tends to be grainy.Slow film stock.
Relatively insensitive to light, it requires a great deal of illumination. The final product tends to look polished.The lens is not wide-angle because there isn’t a great sense of depth, nor are several planes in focus. The lens is probably long but not necessarily a telephoto lens because the depth isn’t inordinately compressed.
The stock is fast because of the grainy quality of the image.
Subsidiary Contrast; where does the eye go next?
The two guns.
How much visual information is packed into the image? Is the texture stark, moderate, or highly detailed?
Minimalist clutter in the warehouse allows a focus on a character driven thriller.
What is the Composition?
Horizontal.
Compositions based on horizontal lines seem visually at rest and suggest placidity or peacefulness.Vertical.
Compositions based on vertical lines seem visually at rest and suggest strength.-> Diagonal.
Compositions based on diagonal, or oblique, lines seem dynamic and suggest tension or anxiety.-> Binary. Binary structures emphasize parallelism.
Triangle.
Triadic compositions stress the dynamic interplay among three mainCircle.
Circular compositions suggest security and enclosure.Is the form open or closed? Does the image suggest a window that arbitrarily isolates a fragment of the scene? Or a proscenium arch, in which the visual elements are carefully arranged and held in balance?
The most nebulous of all the categories of mise en scene, the type of form is determined by how consciously structured the mise en scene is. Open forms stress apparently simple techniques, because with these unself-conscious methods the filmmaker is able to emphasize the immediate, the familiar, the intimate aspects of reality. In open-form images, the frame tends to be deemphasized. In closed form images, all the necessary information is carefully structured within the confines of the frame. Space seems enclosed and self-contained rather than continuous.
Could argue this is a proscenium arch because this is such a classic shot with parallels and juxtapositions.
Is the framing tight or loose? Do the character have no room to move around, or can they move freely without impediments?
Shots where the characters are placed at the edges of the frame and have little room to move around within the frame are considered tight.
Longer shots, in which characters have room to move around within the frame, are considered loose and tend to suggest freedom.
Center-framed giving us the entire scene showing isolation, place and struggle.
Depth of Field. On how many planes is the image composed (how many are in focus)? Does the background or foreground comment in any way on the mid-ground?
Standard DOF, one background and clearly defined foreground.
Which way do the characters look vis-a-vis the camera?
An actor can be photographed in any of five basic positions, each conveying different psychological overtones.
Full-front (facing the camera):
the position with the most intimacy. The character is looking in our direction, inviting our complicity.Quarter Turn:
the favored position of most filmmakers. This position offers a high degree of intimacy but with less emotional involvement than the full-front.-> Profile (looking of the frame left or right):
More remote than the quarter turn, the character in profile seems unaware of being observed, lost in his or her own thoughts.Three-quarter Turn:
More anonymous than the profile, this position is useful for conveying a character’s unfriendly or antisocial feelings, for in effect, the character is partially turning his or her back on us, rejecting our interest.Back to Camera:
The most anonymous of all positions, this position is often used to suggest a character’s alienation from the world. When a character has his or her back to the camera, we can only guess what’s taking place internally, conveying a sense of concealment, or mystery.How much space is there between the characters?
Extremely close, for a gunfight.
The way people use space can be divided into four proxemic patterns.
Intimate distances.
The intimate distance ranges from skin contact to about eighteen inches away. This is the distance of physical involvement–of love, comfort, and tenderness between individuals.-> Personal distances.
The personal distance ranges roughly from eighteen inches away to about four feet away. These distances tend to be reserved for friends and acquaintances. Personal distances preserve the privacy between individuals, yet these rages don’t necessarily suggest exclusion, as intimate distances often do.Social distances.
The social distance rages from four feet to about twelve feet. These distances are usually reserved for impersonal business and casual social gatherings. It’s a friendly range in most cases, yet somewhat more formal than the personal distance.Public distances.
The public distance extends from twelve feet to twenty-five feet or more. This range tends to be formal and rather detached.
DESIGN
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boldtron – 𝗗𝗘𝗣𝗜𝗖𝗧𝗜𝗡𝗚 𝗪𝗔𝗧𝗘𝗥𝗚𝗨𝗡𝗦
Read more: boldtron – 𝗗𝗘𝗣𝗜𝗖𝗧𝗜𝗡𝗚 𝗪𝗔𝗧𝗘𝗥𝗚𝗨𝗡𝗦See this Instagram post by @boldtron using ComfyUI + Krea
https://www.instagram.com/p/C5v-H0PNYYg/?utm_source=ig_web_button_share_sheet
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Goga Tandashvili – bas-relief master
Read more: Goga Tandashvili – bas-relief master@moltenimmersiveart Goga Tandashvili is a master of the art of Bas-Relief. Using this technique, he creates stunning figures that are slightly raised from a flat surface, bringing scenes inspired by the natural world to life. #Art #Artists #GogaTandashvili #BasReliefSculpture #ArtInspiredByNature #ImpressionistArt #BasRelief #Sculptures #Sculptor #Molten #MoltenArt #MoltenImmersiveArt #MoltenAffect #Curation #Curator #ArtCuration #ArtCurator #DorothyDiStefano ♬ original sound – Molten Immersive Art
COLOR
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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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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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No one could see the colour blue until modern times
Read more: No one could see the colour blue until modern timeshttps://www.businessinsider.com/what-is-blue-and-how-do-we-see-color-2015-2
The way that humans see the world… until we have a way to describe something, even something so fundamental as a colour, we may not even notice that something it’s there.
Ancient languages didn’t have a word for blue — not Greek, not Chinese, not Japanese, not Hebrew, not Icelandic cultures. And without a word for the colour, there’s evidence that they may not have seen it at all.
https://www.wnycstudios.org/story/211119-colors
Every language first had a word for black and for white, or dark and light. The next word for a colour to come into existence — in every language studied around the world — was red, the colour of blood and wine.
After red, historically, yellow appears, and later, green (though in a couple of languages, yellow and green switch places). The last of these colours to appear in every language is blue.
The only ancient culture to develop a word for blue was the Egyptians — and as it happens, they were also the only culture that had a way to produce a blue dye.
https://mymodernmet.com/shades-of-blue-color-history/
Considered to be the first ever synthetically produced color pigment, Egyptian blue (also known as cuprorivaite) was created around 2,200 B.C. It was made from ground limestone mixed with sand and a copper-containing mineral, such as azurite or malachite, which was then heated between 1470 and 1650°F. The result was an opaque blue glass which then had to be crushed and combined with thickening agents such as egg whites to create a long-lasting paint or glaze.
If you think about it, blue doesn’t appear much in nature — there aren’t animals with blue pigments (except for one butterfly, Obrina Olivewing, all animals generate blue through light scattering), blue eyes are rare (also blue through light scattering), and blue flowers are mostly human creations. There is, of course, the sky, but is that really blue?
So before we had a word for it, did people not naturally see blue? Do you really see something if you don’t have a word for it?
A researcher named Jules Davidoff traveled to Namibia to investigate this, where he conducted an experiment with the Himba tribe, who speak a language that has no word for blue or distinction between blue and green. When shown a circle with 11 green squares and one blue, they couldn’t pick out which one was different from the others.
When looking at a circle of green squares with only one slightly different shade, they could immediately spot the different one. Can you?
Davidoff says that without a word for a colour, without a way of identifying it as different, it’s much harder for us to notice what’s unique about it — even though our eyes are physically seeing the blocks it in the same way.
Further research brought to wider discussions about color perception in humans. Everything that we make is based on the fact that humans are trichromatic. The television only has 3 colors. Our color printers have 3 different colors. But some people, and in specific some women seemed to be more sensible to color differences… mainly because they’re just more aware or – because of the job that they do.
Eventually this brought to the discovery of a small percentage of the population, referred to as tetrachromats, which developed an extra cone sensitivity to yellow, likely due to gene modifications.
The interesting detail about these is that even between tetrachromats, only the ones that had a reason to develop, label and work with extra color sensitivity actually developed the ability to use their native skills.
So before blue became a common concept, maybe humans saw it. But it seems they didn’t know they were seeing it.
If you see something yet can’t see it, does it exist? Did colours come into existence over time? Not technically, but our ability to notice them… may have…
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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.
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Victor Perez – The Color Management Handbook for Visual Effects Artists
Read more: Victor Perez – The Color Management Handbook for Visual Effects ArtistsDigital Color Principles, Color Management Fundamentals & ACES Workflows
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Capturing the world in HDR for real time projects – Call of Duty: Advanced Warfare
Read more: Capturing the world in HDR for real time projects – Call of Duty: Advanced WarfareReal-World Measurements for Call of Duty: Advanced Warfare
www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
Local version
Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf
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Akiyoshi Kitaoka – Surround biased illumination perception
Read more: Akiyoshi Kitaoka – Surround biased illumination perceptionhttps://x.com/AkiyoshiKitaoka/status/1798705648001327209
The left face appears whitish and the right one blackish, but they are made up of the same luminance.
https://community.wolfram.com/groups/-/m/t/3191015
Illusory staircase Gelb effect
https://www.psy.ritsumei.ac.jp/akitaoka/illgelbe.html
LIGHTING
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What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…
Read more: What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…RASTERIZATION
Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.
In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.
With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.
Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.
Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.
The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.
There are also multiple problems:
If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.
The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.
You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)
Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)
RAY CASTING
It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.
You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.
RAYTRACING
Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.
Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.
Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.
Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.
PATH TRACING
Path tracing can be used to solve more complex lighting situations.
Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.
Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.
blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/
https://en.wikipedia.org/wiki/Rasterisation
https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing
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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) -
domeble – Hi-Resolution CGI Backplates and 360° HDRI
Read more: domeble – Hi-Resolution CGI Backplates and 360° HDRIWhen 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.
-
What is physically correct lighting all about?
Read more: What is physically correct lighting all about?http://gamedev.stackexchange.com/questions/60638/what-is-physically-correct-lighting-all-about
2012-08 Nathan Reed wrote:
Physically-based shading means leaving behind phenomenological models, like the Phong shading model, which are simply built to “look good” subjectively without being based on physics in any real way, and moving to lighting and shading models that are derived from the laws of physics and/or from actual measurements of the real world, and rigorously obey physical constraints such as energy conservation.
For example, in many older rendering systems, shading models included separate controls for specular highlights from point lights and reflection of the environment via a cubemap. You could create a shader with the specular and the reflection set to wildly different values, even though those are both instances of the same physical process. In addition, you could set the specular to any arbitrary brightness, even if it would cause the surface to reflect more energy than it actually received.
In a physically-based system, both the point light specular and the environment reflection would be controlled by the same parameter, and the system would be set up to automatically adjust the brightness of both the specular and diffuse components to maintain overall energy conservation. Moreover you would want to set the specular brightness to a realistic value for the material you’re trying to simulate, based on measurements.
Physically-based lighting or shading includes physically-based BRDFs, which are usually based on microfacet theory, and physically correct light transport, which is based on the rendering equation (although heavily approximated in the case of real-time games).
It also includes the necessary changes in the art process to make use of these features. Switching to a physically-based system can cause some upsets for artists. First of all it requires full HDR lighting with a realistic level of brightness for light sources, the sky, etc. and this can take some getting used to for the lighting artists. It also requires texture/material artists to do some things differently (particularly for specular), and they can be frustrated by the apparent loss of control (e.g. locking together the specular highlight and environment reflection as mentioned above; artists will complain about this). They will need some time and guidance to adapt to the physically-based system.
On the plus side, once artists have adapted and gained trust in the physically-based system, they usually end up liking it better, because there are fewer parameters overall (less work for them to tweak). Also, materials created in one lighting environment generally look fine in other lighting environments too. This is unlike more ad-hoc models, where a set of material parameters might look good during daytime, but it comes out ridiculously glowy at night, or something like that.
Here are some resources to look at for physically-based lighting in games:
SIGGRAPH 2013 Physically Based Shading Course, particularly the background talk by Naty Hoffman at the beginning. You can also check out the previous incarnations of this course for more resources.
Sébastien Lagarde, Adopting a physically-based shading model and Feeding a physically-based shading model
And of course, I would be remiss if I didn’t mention Physically-Based Rendering by Pharr and Humphreys, an amazing reference on this whole subject and well worth your time, although it focuses on offline rather than real-time rendering.
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HDRI shooting and editing by Xuan Prada and Greg Zaal
Read more: HDRI shooting and editing by Xuan Prada and Greg Zaalwww.xuanprada.com/blog/2014/11/3/hdri-shooting
http://blog.gregzaal.com/2016/03/16/make-your-own-hdri/
http://blog.hdrihaven.com/how-to-create-high-quality-hdri/
Shooting checklist
- Full coverage of the scene (fish-eye shots)
- Backplates for look-development (including ground or floor)
- Macbeth chart for white balance
- Grey ball for lighting calibration
- Chrome ball for lighting orientation
- Basic scene measurements
- Material samples
- Individual HDR artificial lighting sources if required
Methodology
- Plant the tripod where the action happens, stabilise it and level it
- Set manual focus
- Set white balance
- Set ISO
- Set raw+jpg
- Set apperture
- Metering exposure
- Set neutral exposure
- Read histogram and adjust neutral exposure if necessary
- Shot slate (operator name, location, date, time, project code name, etc)
- Set auto bracketing
- Shot 5 to 7 exposures with 3 stops difference covering the whole environment
- Place the aromatic kit where the tripod was placed, and take 3 exposures. Keep half of the grey sphere hit by the sun and half in shade.
- Place the Macbeth chart 1m away from tripod on the floor and take 3 exposures
- Take backplates and ground/floor texture references
- Shoot reference materials
- Write down measurements of the scene, specially if you are shooting interiors.
- If shooting artificial lights take HDR samples of each individual lighting source.
Exposures starting point
- Day light sun visible ISO 100 F22
- Day light sun hidden ISO 100 F16
- Cloudy ISO 320 F16
- Sunrise/Sunset ISO 100 F11
- Interior well lit ISO 320 F16
- Interior ambient bright ISO 320 F10
- Interior bad light ISO 640 F10
- Interior ambient dark ISO 640 F8
- Low light situation ISO 640 F5
NOTE: The goal is to clean the initial individual brackets before or at merging time as much as possible.
This means:- keeping original shooting metadata
- de-fringing
- removing aberration (through camera lens data or automatically)
- at 32 bit
- in ACEScg (or ACES) wherever possible
Here are the tips for using the chromatic ball in VFX projects, written in English:
https://www.linkedin.com/posts/bellrodrigo_here-are-the-tips-for-using-the-chromatic-activity-7200950595438940160-AGBpTips for Using the Chromatic Ball in VFX Projects**
The chromatic ball is an invaluable tool in VFX work, helping to capture lighting and reflection data crucial for integrating CGI elements seamlessly. Here are some tips to maximize its effectiveness:
1. **Positioning**:
– Place the chromatic ball in the same lighting conditions as the main subject. Ensure it is visible in the camera frame but not obstructing the main action.
– Ideally, place the ball where the CGI elements will be integrated to match the lighting and reflections accurately.2. **Recording Reference Footage**:
– Capture reference footage of the chromatic ball at the beginning and end of each scene or lighting setup. This ensures you have consistent lighting data for the entire shoot.3. **Consistent Angles**:
– Use consistent camera angles and heights when recording the chromatic ball. This helps in comparing and matching lighting setups across different shots.4. **Combine with a Gray Ball**:
– Use a gray ball alongside the chromatic ball. The gray ball provides a neutral reference for exposure and color balance, complementing the chromatic ball’s reflection data.5. **Marking Positions**:
– Mark the position of the chromatic ball on the set to ensure consistency when shooting multiple takes or different camera angles.6. **Lighting Analysis**:
– Analyze the chromatic ball footage to understand the light sources, intensity, direction, and color temperature. This information is crucial for creating realistic CGI lighting and shadows.7. **Reflection Analysis**:
– Use the chromatic ball to capture the environment’s reflections. This helps in accurately reflecting the CGI elements within the same scene, making them blend seamlessly.8. **Use HDRI**:
– Capture High Dynamic Range Imagery (HDRI) of the chromatic ball. HDRI provides detailed lighting information and can be used to light CGI scenes with greater realism.9. **Communication with VFX Team**:
– Ensure that the VFX team is aware of the chromatic ball’s data and how it was captured. Clear communication ensures that the data is used effectively in post-production.10. **Post-Production Adjustments**:
– In post-production, use the chromatic ball data to adjust the CGI elements’ lighting and reflections. This ensures that the final output is visually cohesive and realistic.
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