COMPOSITION
DESIGN
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Pantheon of the War – The colossal war paintingRead more: Pantheon of the War – The colossal war paintingFour years in the making with the help of 150 artists, in commemoration of WW1. edition.cnn.com/style/article/pantheon-de-la-guerre-wwi-painting/index.html A panoramic canvas measuring 402 feet (122 meters) around and 45 feet (13.7 meters) high. It contained over 5,000 life-size portraits of war heroes, royalty and government officials from the Allies of World War I. Partial section upload: 
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Interactive Maps of Earthquakes around the worldRead more: Interactive Maps of Earthquakes around the worldhttps://ralucanicola.github.io/JSAPI_demos/earthquakes https://ralucanicola.github.io/JSAPI_demos/earthquakes-depth https://ralucanicola.github.io/JSAPI_demos/ridgecrest-earthquake https://ralucanicola.github.io/JSAPI_demos/last-earthquakes  
COLOR
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Björn Ottosson – OKHSV and OKHSL – Two new color spaces for color pickingRead 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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Akiyoshi Kitaoka – Surround biased illumination perceptionRead 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
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colorhunt.coRead more: colorhunt.coColor Hunt is a free and open platform for color inspiration with thousands of trendy hand-picked color palettes.  
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Tim Kang – calibrated white light values in sRGB color spaceRead more: Tim Kang – calibrated white light values in sRGB color space8bit sRGB encoded 
 2000K 255 139 22
 2700K 255 172 89
 3000K 255 184 109
 3200K 255 190 122
 4000K 255 211 165
 4300K 255 219 178
 D50 255 235 205
 D55 255 243 224
 D5600 255 244 227
 D6000 255 249 240
 D65 255 255 255
 D10000 202 221 255
 D20000 166 196 2558bit Rec709 Gamma 2.4 
 2000K 255 145 34
 2700K 255 177 97
 3000K 255 187 117
 3200K 255 193 129
 4000K 255 214 170
 4300K 255 221 182
 D50 255 236 208
 D55 255 243 226
 D5600 255 245 229
 D6000 255 250 241
 D65 255 255 255
 D10000 204 222 255
 D20000 170 199 2558bit Display P3 encoded 
 2000K 255 154 63
 2700K 255 185 109
 3000K 255 195 127
 3200K 255 201 138
 4000K 255 219 176
 4300K 255 225 187
 D50 255 239 212
 D55 255 245 228
 D5600 255 246 231
 D6000 255 251 242
 D65 255 255 255
 D10000 208 223 255
 D20000 175 199 25510bit Rec2020 PQ (100 nits) 
 2000K 520 435 273
 2700K 520 466 358
 3000K 520 475 384
 3200K 520 480 399
 4000K 520 495 446
 4300K 520 500 458
 D50 520 510 482
 D55 520 514 497
 D5600 520 514 500
 D6000 520 517 509
 D65 520 520 520
 D10000 479 489 520
 D20000 448 464 520
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The Forbidden colors – Red-Green & Blue-Yellow: The Stunning Colors You Can’t SeeRead more: The Forbidden colors – Red-Green & Blue-Yellow: The Stunning Colors You Can’t Seewww.livescience.com/17948-red-green-blue-yellow-stunning-colors.html  While the human eye has red, green, and blue-sensing cones, those cones are cross-wired in the retina to produce a luminance channel plus a red-green and a blue-yellow channel, and it’s data in that color space (known technically as “LAB”) that goes to the brain. That’s why we can’t perceive a reddish-green or a yellowish-blue, whereas such colors can be represented in the RGB color space used by digital cameras. https://en.rockcontent.com/blog/the-use-of-yellow-in-data-design The back of the retina is covered in light-sensitive neurons known as cone cells and rod cells. There are three types of cone cells, each sensitive to different ranges of light. These ranges overlap, but for convenience the cones are referred to as blue (short-wavelength), green (medium-wavelength), and red (long-wavelength). The rod cells are primarily used in low-light situations, so we’ll ignore those for now. When light enters the eye and hits the cone cells, the cones get excited and send signals to the brain through the visual cortex. Different wavelengths of light excite different combinations of cones to varying levels, which generates our perception of color. You can see that the red cones are most sensitive to light, and the blue cones are least sensitive. The sensitivity of green and red cones overlaps for most of the visible spectrum.  Here’s how your brain takes the signals of light intensity from the cones and turns it into color information. To see red or green, your brain finds the difference between the levels of excitement in your red and green cones. This is the red-green channel. To get “brightness,” your brain combines the excitement of your red and green cones. This creates the luminance, or black-white, channel. To see yellow or blue, your brain then finds the difference between this luminance signal and the excitement of your blue cones. This is the yellow-blue channel. From the calculations made in the brain along those three channels, we get four basic colors: blue, green, yellow, and red. Seeing blue is what you experience when low-wavelength light excites the blue cones more than the green and red. Seeing green happens when light excites the green cones more than the red cones. Seeing red happens when only the red cones are excited by high-wavelength light. Here’s where it gets interesting. Seeing yellow is what happens when BOTH the green AND red cones are highly excited near their peak sensitivity. This is the biggest collective excitement that your cones ever have, aside from seeing pure white. Notice that yellow occurs at peak intensity in the graph to the right. Further, the lens and cornea of the eye happen to block shorter wavelengths, reducing sensitivity to blue and violet light. 
LIGHTING
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GretagMacbeth Color Checker Numeric Values and Middle GrayRead more: GretagMacbeth Color Checker Numeric Values and Middle GrayThe human eye perceives half scene brightness not as the linear 50% of the present energy (linear nature values) but as 18% of the overall brightness. We are biased to perceive more information in the dark and contrast areas. A Macbeth chart helps with calibrating back into a photographic capture into this “human perspective” of the world. https://en.wikipedia.org/wiki/Middle_gray In photography, painting, and other visual arts, middle gray or middle grey is a tone that is perceptually about halfway between black and white on a lightness scale in photography and printing, it is typically defined as 18% reflectance in visible light  Light meters, cameras, and pictures are often calibrated using an 18% gray card[4][5][6] or a color reference card such as a ColorChecker. On the assumption that 18% is similar to the average reflectance of a scene, a grey card can be used to estimate the required exposure of the film. https://en.wikipedia.org/wiki/ColorChecker (more…)
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Practical Aspects of Spectral Data and LEDs in Digital Content Production and Virtual Production – SIGGRAPH 2022Read more: Practical Aspects of Spectral Data and LEDs in Digital Content Production and Virtual Production – SIGGRAPH 2022Comparison to the commercial side  https://www.ecolorled.com/blog/detail/what-is-rgb-rgbw-rgbic-strip-lights RGBW (RGB + White) LED strip uses a 4-in-1 LED chip made up of red, green, blue, and white. RGBWW (RGB + White + Warm White) LED strip uses either a 5-in-1 LED chip with red, green, blue, white, and warm white for color mixing. The only difference between RGBW and RGBWW is the intensity of the white color. The term RGBCCT consists of RGB and CCT. CCT (Correlated Color Temperature) means that the color temperature of the led strip light can be adjusted to change between warm white and white. Thus, RGBWW strip light is another name of RGBCCT strip. RGBCW is the acronym for Red, Green, Blue, Cold, and Warm. These 5-in-1 chips are used in supper bright smart LED lighting products 
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About green screensRead more: About green screenshackaday.com/2015/02/07/how-green-screen-worked-before-computers/ www.newtek.com/blog/tips/best-green-screen-materials/ www.chromawall.com/blog//chroma-key-green Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS). Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production: Green Screen as RGB Color Value: 0, 177, 64 
 Green Screen as CMYK Color Value: 81, 0, 92, 0
 Green Screen as Hex Color Value: #00b140
 Green Screen as Websafe Color Value: #009933Chroma Key Green is reasonably close to an 18% gray reflectance. Illuminate your green screen with an uniform source with less than 2/3 EV variation. 
 The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting.
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9 Best Hacks to Make a Cinematic Video with Any CameraRead more: 9 Best Hacks to Make a Cinematic Video with Any Camerahttps://www.flexclip.com/learn/cinematic-video.html - Frame Your Shots to Create Depth
- Create Shallow Depth of Field
- Avoid Shaky Footage and Use Flexible Camera Movements
- Properly Use Slow Motion
- Use Cinematic Lighting Techniques
- Apply Color Grading
- Use Cinematic Music and SFX
- Add Cinematic Fonts and Text Effects
- Create the Cinematic Bar at the Top and the Bottom
  
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Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View SynthesisRead more: Lighting Every Darkness with 3DGS: Fast Training and Real-Time Rendering and Denoising for HDR View Synthesishttps://srameo.github.io/projects/le3d/ LE3D is a method for real-time HDR view synthesis from RAW images. It is particularly effective for nighttime scenes. https://github.com/Srameo/LE3D 
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