COMPOSITION
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Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental processRead more: Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental processhttps://www.chrbutler.com/understanding-the-eye-mind-connection The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies. This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes. Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles: - Attention and Engagement
- Visual Hierarchy
- Cognitive Load Management
- Context and Meaning
  
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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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Hand made Settlers of Catan boardRead more: Hand made Settlers of Catan boardtrytrytry.de/2016/07/mega-diy-siedler-von-catan-in-3d/ https://www.youtube.com/watch?v=Lqww2yZkeh0 
COLOR
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Scene Referred vs Display Referred color workflowsRead more: Scene Referred vs Display Referred color workflowsDisplay Referred it is tied to the target hardware, as such it bakes color requirements into every type of media output request. Scene Referred uses a common unified wide gamut and targeting audience through CDL and DI libraries instead. 
 So that color information stays untouched and only “transformed” as/when needed.Sources: 
 – Victor Perez – Color Management Fundamentals & ACES Workflows in Nuke
 – https://z-fx.nl/ColorspACES.pdf
 – Wicus
 
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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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Gamma correctionRead more: Gamma correction http://www.normankoren.com/makingfineprints1A.html#Gammabox https://en.wikipedia.org/wiki/Gamma_correction http://www.photoscientia.co.uk/Gamma.htm https://www.w3.org/Graphics/Color/sRGB.html http://www.eizoglobal.com/library/basics/lcd_display_gamma/index.html https://forum.reallusion.com/PrintTopic308094.aspx Basically, gamma is the relationship between the brightness of a pixel as it appears on the screen, and the numerical value of that pixel. Generally Gamma is just about defining relationships. Three main types: 
 – Image Gamma encoded in images
 – Display Gammas encoded in hardware and/or viewing time
 – System or Viewing Gamma which is the net effect of all gammas when you look back at a final image. In theory this should flatten back to 1.0 gamma.
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Photography Basics : Spectral Sensitivity Estimation Without a CameraRead 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.  
LIGHTING
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Convert between light exposure and intensityRead more: Convert between light exposure and intensityimport math,sys def Exposure2Intensity(exposure): exp = float(exposure) result = math.pow(2,exp) print(result) Exposure2Intensity(0) def Intensity2Exposure(intensity): inarg = float(intensity) if inarg == 0: print("Exposure of zero intensity is undefined.") return if inarg < 1e-323: inarg = max(inarg, 1e-323) print("Exposure of negative intensities is undefined. Clamping to a very small value instead (1e-323)") result = math.log(inarg, 2) print(result) Intensity2Exposure(0.1)Why Exposure?Exposure is a stop value that multiplies the intensity by 2 to the power of the stop. Increasing exposure by 1 results in double the amount of light. 
 Artists think in “stops.” Doubling or halving brightness is easy math and common in grading and look-dev.
 Exposure counts doublings in whole stops:- +1 stop = ×2 brightness
- −1 stop = ×0.5 brightness
 This gives perceptually even controls across both bright and dark values. 
 Why Intensity?Intensity is linear. 
 It’s what render engines and compositors expect when:- Summing values
- Averaging pixels
- Multiplying or filtering pixel data
 Use intensity when you need the actual math on pixel/light data. 
 Formulas (from your Python)- Intensity from exposure: intensity = 2**exposure
- Exposure from intensity: exposure = log₂(intensity)
 Guardrails: - Intensity must be > 0 to compute exposure.
- If intensity = 0 → exposure is undefined.
- Clamp tiny values (e.g. 1e−323) before using log₂.
 
 Use Exposure (stops) when…- You want artist-friendly sliders (−5…+5 stops)
- Adjusting look-dev or grading in even stops
- Matching plates with quick ±1 stop tweaks
- Tweening brightness changes smoothly across ranges
 
 Use Intensity (linear) when…- Storing raw pixel/light values
- Multiplying textures or lights by a gain
- Performing sums, averages, and filters
- Feeding values to render engines expecting linear data
 
 Examples- +2 stops → 2**2 = 4.0 (×4)
- +1 stop → 2**1 = 2.0 (×2)
- 0 stop → 2**0 = 1.0 (×1)
- −1 stop → 2**(−1) = 0.5 (×0.5)
- −2 stops → 2**(−2) = 0.25 (×0.25)
- Intensity 0.1 → exposure = log₂(0.1) ≈ −3.32
 
 Rule of thumbThink in stops (exposure) for controls and matching. 
 Compute in linear (intensity) for rendering and math.
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Neural Microfacet Fields for Inverse RenderingRead more: Neural Microfacet Fields for Inverse Renderinghttps://half-potato.gitlab.io/posts/nmf/ 
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Capturing the world in HDR for real time projects – Call of Duty: Advanced WarfareRead 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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