COMPOSITION
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Photography basics: Camera Aspect Ratio, Sensor Size and Depth of Field – resolutionsRead more: Photography basics: Camera Aspect Ratio, Sensor Size and Depth of Field – resolutionshttp://www.shutterangle.com/2012/cinematic-look-aspect-ratio-sensor-size-depth-of-field/ http://www.shutterangle.com/2012/film-video-aspect-ratio-artistic-choice/ 
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Composition – cinematography Cheat SheetRead 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.
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 What type of lighting?
DESIGN
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Chongqing the world’s largest city in picturesRead more: Chongqing the world’s largest city in pictureshttps://www.theguardian.com/world/gallery/2025/apr/27/chongqing-the-worlds-largest-city-in-pictures The largest city in the world is as big as Austria, but few people have ever heard of it. The megacity of 34 million people in central of China is the emblem of the fastest urban revolution on the planet.     
COLOR
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Thomas Mansencal – Colour Science for PythonRead more: Thomas Mansencal – Colour Science for Pythonhttps://thomasmansencal.substack.com/p/colour-science-for-python https://www.colour-science.org/ Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science. It is freely available under the BSD-3-Clause terms. 
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Paul Debevec, Chloe LeGendre, Lukas Lepicovsky – Jointly Optimizing Color Rendition and In-Camera Backgrounds in an RGB Virtual Production StageRead more: Paul Debevec, Chloe LeGendre, Lukas Lepicovsky – Jointly Optimizing Color Rendition and In-Camera Backgrounds in an RGB Virtual Production Stagehttps://arxiv.org/pdf/2205.12403.pdf RGB LEDs vs RGBWP (RGB + lime + phospor converted amber) LEDs Local copy: 
 
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Rec-2020 – TVs new color gamut standard used by Dolby Vision?Read more: Rec-2020 – TVs new color gamut standard used by Dolby Vision?https://www.hdrsoft.com/resources/dri.html#bit-depth  The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to. For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene. For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise. For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen. The Dynamic Range of real-world scenes can be quite high — ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel. Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera. For TVs HDR is great, but it’s not the only new TV feature worth discussing. (more…)
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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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Is a MacBeth Colour Rendition Chart the Safest Way to Calibrate a Camera?Read more: Is a MacBeth Colour Rendition Chart the Safest Way to Calibrate a Camera?www.colour-science.org/posts/the-colorchecker-considered-mostly-harmless/ “Unless you have all the relevant spectral measurements, a colour rendition chart should not be used to perform colour-correction of camera imagery but only for white balancing and relative exposure adjustments.” “Using a colour rendition chart for colour-correction might dramatically increase error if the scene light source spectrum is different from the illuminant used to compute the colour rendition chart’s reference values.” “other factors make using a colour rendition chart unsuitable for camera calibration: – Uncontrolled geometry of the colour rendition chart with the incident illumination and the camera. 
 – Unknown sample reflectances and ageing as the colour of the samples vary with time.
 – Low samples count.
 – Camera noise and flare.
 – Etc…“Those issues are well understood in the VFX industry, and when receiving plates, we almost exclusively use colour rendition charts to white balance and perform relative exposure adjustments, i.e. plate neutralisation.” 
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3D Lighting Tutorial by Amaan KramRead more: 3D Lighting Tutorial by Amaan Kramhttp://www.amaanakram.com/lightingT/part1.htm 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
 · SizeThe overall thrust of this writing is to produce photo-realistic images by applying good lighting techniques. 
LIGHTING
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Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs IlluminanceRead more: Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs Illuminancehttps://www.translatorscafe.com/unit-converter/en-US/illumination/1-11/ The power output of a light source is measured using the unit of watts W. This is a direct measure to calculate how much power the light is going to drain from your socket and it is not relatable to the light brightness itself. The amount of energy emitted from it per second. That energy comes out in a form of photons which we can crudely represent with rays of light coming out of the source. The higher the power the more rays emitted from the source in a unit of time. Not all energy emitted is visible to the human eye, so we often rely on photometric measurements, which takes in account the sensitivity of human eye to different wavelenghts Details in the post 
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Photography basics: Lumens vs Candelas (candle) vs Lux vs FootCandle vs Watts vs Irradiance vs Illuminance
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Gamma correction
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sRGB vs REC709 – An introduction and FFmpeg implementations
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Sensitivity of human eye
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