COMPOSITION
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StudioBinder – Roger Deakins on How to Choose a Camera Lens — Cinematography Composition Techniques
Read more: StudioBinder – Roger Deakins on How to Choose a Camera Lens — Cinematography Composition Techniques
https://www.studiobinder.com/blog/camera-lens-buying-guide/
https://www.studiobinder.com/blog/e-books/camera-lenses-explained-volume-1-ebook
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Key/Fill ratios and scene composition using false colors
Read more: Key/Fill ratios and scene composition using false colors
To measure the contrast ratio you will need a light meter. The process starts with you measuring the main source of light, or the key light.
Get a reading from the brightest area on the face of your subject. Then, measure the area lit by the secondary light, or fill light. To make sense of what you have just measured you have to understand that the information you have just gathered is in F-stops, a measure of light. With each additional F-stop, for example going one stop from f/1.4 to f/2.0, you create a doubling of light. The reverse is also true; moving one stop from f/8.0 to f/5.6 results in a halving of the light.
Let’s say you grabbed a measurement from your key light of f/8.0. Then, when you measured your fill light area, you get a reading of f/4.0. This will lead you to a contrast ratio of 4:1 because there are two stops between f/4.0 and f/8.0 and each stop doubles the amount of light. In other words, two stops x twice the light per stop = four times as much light at f/8.0 than at f/4.0.
theslantedlens.com/2017/lighting-ratios-photo-video/
Examples in the post
DESIGN
COLOR
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Pattern generators
Read more: Pattern generatorshttp://qrohlf.com/trianglify-generator/
https://halftonepro.com/app/polygons#
https://mattdesl.svbtle.com/generative-art-with-nodejs-and-canvas
https://www.patterncooler.com/
http://permadi.com/java/spaint/spaint.html
https://dribbble.com/shots/1847313-Kaleidoscope-Generator-PSD
http://eskimoblood.github.io/gerstnerizer/
http://www.stripegenerator.com/
http://btmills.github.io/geopattern/geopattern.html
http://fractalarchitect.net/FA4-Random-Generator.html
https://sciencevsmagic.net/fractal/#0605,0000,3,2,0,1,2
https://sites.google.com/site/mandelbulber/home
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OLED vs QLED – What TV is better?
Read more: OLED vs QLED – What TV is better?
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.
When analyzing TVs color, it may be beneficial to consider at least 3 elements:
“Color Depth”, “Color Gamut”, and “Dynamic Range”.Color Depth (or “Bit-Depth”, e.g. 8-bit, 10-bit, 12-bit) determines how many distinct color variations (tones/shades) can be viewed on a given display.
Color Gamut (e.g. WCG) determines which specific colors can be displayed from a given “Color Space” (Rec.709, Rec.2020, DCI-P3) (i.e. the color range).
Dynamic Range (SDR, HDR) determines the luminosity range of a specific color – from its darkest shade (or tone) to its brightest.
The overall brightness range of a color will be determined by a display’s “contrast ratio”, that is, the ratio of luminance between the darkest black that can be produced and the brightest white.
Color Volume is the “Color Gamut” + the “Dynamic/Luminosity Range”.
A TV’s Color Volume will not only determine which specific colors can be displayed (the color range) but also that color’s luminosity range, which will have an affect on its “brightness”, and “colorfulness” (intensity and saturation).The better the colour volume in a TV, the closer to life the colours appear.
QLED TV can express nearly all of the colours in the DCI-P3 colour space, and of those colours, express 100% of the colour volume, thereby producing an incredible range of colours.
With OLED TV, when the image is too bright, the percentage of the colours in the colour volume produced by the TV drops significantly. The colours get washed out and can only express around 70% colour volume, making the picture quality drop too.
Note. OLED TV uses organic material, so it may lose colour expression as it ages.
Resources for more reading and comparison below
www.avsforum.com/forum/166-lcd-flat-panel-displays/2812161-what-color-volume.html
www.newtechnologytv.com/qled-vs-oled/
news.samsung.com/za/qled-tv-vs-oled-tv
www.cnet.com/news/qled-vs-oled-samsungs-tv-tech-and-lgs-tv-tech-are-not-the-same/
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HDR and Color
Read more: HDR and Colorhttps://www.soundandvision.com/content/nits-and-bits-hdr-and-color
In HD we often refer to the range of available colors as a color gamut. Such a color gamut is typically plotted on a two-dimensional diagram, called a CIE chart, as shown in at the top of this blog. Each color is characterized by its x/y coordinates.
Good enough for government work, perhaps. But for HDR, with its higher luminance levels and wider color, the gamut becomes three-dimensional.
For HDR the color gamut therefore becomes a characteristic we now call the color volume. It isn’t easy to show color volume on a two-dimensional medium like the printed page or a computer screen, but one method is shown below. As the luminance becomes higher, the picture eventually turns to white. As it becomes darker, it fades to black. The traditional color gamut shown on the CIE chart is simply a slice through this color volume at a selected luminance level, such as 50%.
Three different color volumes—we still refer to them as color gamuts though their third dimension is important—are currently the most significant. The first is BT.709 (sometimes referred to as Rec.709), the color gamut used for pre-UHD/HDR formats, including standard HD.
The largest is known as BT.2020; it encompasses (roughly) the range of colors visible to the human eye (though ET might find it insufficient!).
Between these two is the color gamut used in digital cinema, known as DCI-P3.
sRGB
D65
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Yasuharu YOSHIZAWA – Comparison of sRGB vs ACREScg in Nuke
Read more: Yasuharu YOSHIZAWA – Comparison of sRGB vs ACREScg in NukeAnswering the question that is often asked, “Do I need to use ACEScg to display an sRGB monitor in the end?” (Demonstration shown at an in-house seminar)
Comparison of scanlineRender output with extreme color lights on color charts with sRGB/ACREScg in color – OCIO -working space in NukeDownload the Nuke script:
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Björn Ottosson – OKlch color space
Read more: Björn Ottosson – OKlch color spaceBjörn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
- L: Lightness (the perceived brightness of the color)
- C: Chroma (the intensity or saturation of the color)
- H: Hue (the actual color, such as red, blue, green, etc.)
Also read:
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Is it possible to get a dark yellow
Read more: Is it possible to get a dark yellowhttps://www.patreon.com/posts/102660674
https://www.linkedin.com/posts/stephenwestland_here-is-a-post-about-the-dark-yellow-problem-activity-7187131643764092929-7uCL
LIGHTING
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
Read more: DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ballhttps://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“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.”
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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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