COMPOSITION
- 
Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental process
Read 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
 

 - 
Photography basics: Camera Aspect Ratio, Sensor Size and Depth of Field – resolutions
Read 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/
 
DESIGN
COLOR
LIGHTING
- 
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…) - 
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.”

 - 
How to Direct and Edit a Fight Scene for Rhythm and Pacing
Read more: How to Direct and Edit a Fight Scene for Rhythm and Pacingwww.premiumbeat.com/blog/directing-fight-scene-cinematography/
1- Frame the action
2- Stage the action
3- Use camera movements
4- Set a rhythm
5- Control the speed of the action
 - 
Convert between light exposure and intensity
Read 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 thumb
Think in stops (exposure) for controls and matching.
Compute in linear (intensity) for rendering and math. 
COLLECTIONS
| Featured AI
| Design And Composition 
| Explore posts  
POPULAR SEARCHES
unreal | pipeline | virtual production | free | learn | photoshop | 360 | macro | google | nvidia | resolution | open source | hdri | real-time | photography basics | nuke
FEATURED POSTS
- 
Want to build a start up company that lasts? Think three-layer cake
 - 
Types of Film Lights and their efficiency – CRI, Color Temperature and Luminous Efficacy
 - 
copypastecharacter.com – alphabets, special characters, alt codes and symbols library
 - 
Daniele Tosti Interview for the magazine InCG, Taiwan, Issue 28, 201609
 - 
What Is The Resolution and view coverage Of The human Eye. And what distance is TV at best?
 - 
AI and the Law – studiobinder.com – What is Fair Use: Definition, Policies, Examples and More
 - 
PixelSham – Introduction to Python 2022
 - 
The Perils of Technical Debt – Understanding Its Impact on Security, Usability, and Stability
 
Social Links
DISCLAIMER – Links and images on this website may be protected by the respective owners’ copyright. All data submitted by users through this site shall be treated as freely available to share.











