Category: Featured

In software development, “technical debt” is a term used to describe the accumulation of shortcuts, suboptimal solutions, and outdated code that occur as developers rush to meet deadlines or prioritize immediate goals over long-term maintainability. While this concept initially seems abstract, its consequences are concrete and can significantly affect the security, usability, and stability of software systems.

The Nature of Technical Debt

Technical debt arises when software engineers choose a less-than-ideal implementation in the interest of saving time or reducing upfront effort. Much like financial debt, these decisions come with an interest rate: over time, the cost of maintaining and updating the system increases, and more effort is required to fix problems that stem from earlier choices. In extreme cases, technical debt can slow development to a crawl, causing future updates or improvements to become far more difficult than they would have been with cleaner, more scalable code.

Impact on Security

One of the most significant threats posed by technical debt is the vulnerability it creates in terms of software security. Outdated code often lacks the latest security patches or is built on legacy systems that are no longer supported. Attackers can exploit these weaknesses, leading to data breaches, ransomware, or other forms of cybercrime. Furthermore, as systems grow more complex and the debt compounds, identifying and fixing vulnerabilities becomes increasingly challenging. Failing to address technical debt leaves an organization exposed to security risks that may only become apparent after a costly incident.

Impact on Usability

Technical debt also affects the user experience. Systems burdened by outdated code often become clunky and slow, leading to poor usability. Engineers may find themselves continuously patching minor issues rather than implementing larger, user-centric improvements. Over time, this results in a product that feels antiquated, is difficult to use, or lacks modern functionality. In a competitive market, poor usability can alienate users, causing a loss of confidence and driving them to alternative products or services.

Impact on Stability

Stability is another critical area impacted by technical debt. As developers add features or make updates to systems weighed down by previous quick fixes, they run the risk of introducing bugs or causing system crashes. The tangled, fragile nature of code laden with technical debt makes troubleshooting difficult and increases the likelihood of cascading failures. Over time, instability in the software can erode both the trust of users and the efficiency of the development team, as more resources are dedicated to resolving recurring issues rather than innovating or expanding the system’s capabilities.

The Long-Term Costs of Ignoring Technical Debt

While technical debt can provide short-term gains by speeding up initial development, the long-term costs are much higher. Unaddressed technical debt can lead to project delays, escalating maintenance costs, and an ever-widening gap between current code and modern best practices. The more technical debt accumulates, the harder and more expensive it becomes to address. For many companies, failing to pay down this debt eventually results in a critical juncture: either invest heavily in refactoring the codebase or face an expensive overhaul to rebuild from the ground up.

Conclusion

Technical debt is an unavoidable aspect of software development, but understanding its perils is essential for minimizing its impact on security, usability, and stability. By actively managing technical debt—whether through regular refactoring, code audits, or simply prioritizing long-term quality over short-term expedience—organizations can avoid the most dangerous consequences and ensure their software remains robust and reliable in an ever-changing technological landscape.

The Holy Grail – https://github.com/ad-si/awesome-3d-printing

• Thingiverse – https://www.thingiverse.com/
• Makerworld – https://makerworld.com/
• Printables – https://www.printables.com/
• Cults – https://cults3d.com/
• CG Trader – https://www.cgtrader.com/3d-print-models
• Sketchfab – https://sketchfab.com/store/3d-models/stl
• 3D Export – https://3dexport.com/
• MyMiniFactory – https://www.myminifactory.com/
• Thangs – https://thangs.com/
• Yeggi – https://www.yeggi.com/
• FAB365 – https://fab365.net/
• Gambody – https://www.gambody.com/
• All3DP News – https://all3dp.com/
• TCT Magazine –  https://www.tctmagazine.com/topics/3D-printing-news/
• 3DPrint.com – https://3dprint.com/
• NASA 3D Models – https://nasa3d.arc.nasa.gov/models/printable

• HyperHuman Rodin – free and pay per use
• LumaLabs Genie – free, web based
• Vectary – free and monthly fees, web based
• Selva3D – pay per use
• 3D-Tool – one off payment
• Insight3d – free and open-source
• ItsLitho – free, web based
• Blender – free
• SculptGL – free, web based
• Embossify – pay per use, web based
• Smoothie-3D – free, web based
• ZW3D – one off payment
• ImageToSTL – free, web based
• Alpha3D – First 50 AI-generated 3D assets free
• Reliefmod – free, web based
• 3D Builder – free
• Cube by CSM.ai – web based
• Kaedim3D – monthly and pay per use fees
• 3DForPrint – free, web based
• ZoeDepth – free, web based
• ZoeDepth Colab notebook –  easy to use interface for the depth estimation model “ZoeDepth”
• TilingZoeDepth – higher resolution free web based
• DreamGaussian – free, web based
• Photoshop Neural Filters – monthly fees
• DepthR – pay per use
• Materialize – open source
• VistaSculpt – monthly fees
• STL2PNG Converter – free
• Hi3D – free
• Meshy.ai – Text to 3D – free and pay per use

https://www.news.viverse.com/post/pixel-to-polygon-converting-2d-images-to-3d-models-top-tools-revealed

https://www.rankred.com/convert-2d-images-to-3d/

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Most common ways to smooth 3D prints

https://www.xometry.com/resources/3d-printing/smooth-3d-prints/

1. Using Paint and Sanding Material for PLA filaments
2. Using Abrasive Smoothing Methods
3. Using XTC-3D epoxy resin
4. Using 3D Gloop
5. Using PolyMaker PolySmooth PVB Filament
6. Using Chemical Smoothing like Resin, Ethyl Acetate, Acetone for ABS filaments or Isopropyl Alcohol for PVB filaments
7. Using Heat Gun
8. Using hardware like Polymaker Polysher
9. Automotive Spray Putty for PLA
10. Using bees wax
11. Changing inner/outer order

Note: Some methods apply to only a few types of 3D print technologies

3D Printing Hack – Smoothing 3D Prints with Resin

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https://www.fastcompany.com/91131427/want-to-build-a-company-that-lasts-think-three-layer-cake

Building a successful business requires a focus on three key elements: product excellence, go-to-market strategy, and operational excellence. Neglecting any of these areas can lead to failure, as evidenced by the high percentage of startups that don’t make it past the five-year mark. Founders and CEOs must ensure a solid product foundation while also integrating effective sales, marketing, and management strategies to achieve sustainable growth and scale.

• Foundation: Product Excellence, Core Values and Mission
  • Core Values: These are the guiding principles that dictate behavior and action within the company. They form the ethical foundation and are crucial for maintaining consistency in decision-making.
  • Mission: This defines the company’s purpose and goals. A clear and compelling mission helps align the team and provides a sense of direction.
  • Efficiency and Scalability: This layer focuses on creating efficient processes that can scale as the company grows. Streamlined operations reduce costs and increase productivity.

• Structure: Operational Excellence and Innovation
  • Operational Excellence: Efficient processes, quality control, and continuous improvement fall into this layer. Ensuring that the company operates smoothly and effectively is crucial for sustainability.
  • Innovation: Staying competitive requires innovation. This involves developing new products, services, or processes that add value and keep the company relevant in the market.
  • Quality Control and Continuous Improvement: Ensuring that operational processes are of high quality and constantly improving helps maintain product excellence and customer satisfaction.
  • Technology and Infrastructure: Investing in the right technology and infrastructure to support business operations is vital. This includes everything from manufacturing equipment to software systems that enhance operational efficiency.

• Strategy: Go-to-Market Strategy, Vision and Long-Term Planning
  • Vision: A forward-looking vision inspires and motivates the team. It outlines where the company aims to be in the future and helps in setting long-term goals.
  • Strategic Planning: This involves setting long-term goals and determining the actions and resources needed to achieve them. It includes market analysis, competitive strategy, and growth planning.
  • Market Understanding: A deep understanding of the target market, including customer segments, competitors, and market trends, is essential. This knowledge helps in positioning the product effectively.
  • Marketing and Sales Execution: This involves creating a robust marketing plan that includes branding, messaging, and advertising strategies to attract and retain customers. Additionally, building a strong sales strategy ensures that the product reaches the right customers through the right channels.
  • Customer Acquisition and Retention: Effective strategies for acquiring new customers and retaining existing ones are critical. This includes loyalty programs, customer service excellence, and engagement initiatives.

https://docs.godotengine.org/en/stable/tutorials/scripting/gdscript/gdscript_basics.html

https://www.canva.com/design/DAGBWXOIWXY/hW1uECYrkiyqs9rN0a-XIA/view?utm_content=DAGBWXOIWXY

https://www.reddit.com/r/godot/comments/18aid4u/unit_circle_in_godot_format_version_2_by_foxsinart/

Images in the post

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https://education.civitai.com/generative-ai-glossary/

The 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

https://photo.stackexchange.com/questions/968/how-can-i-correctly-measure-light-using-a-built-in-camera-meter

The exposure meter in the camera does not know whether the subject itself is bright or not. It simply measures the amount of light that comes in, and makes a guess based on that. The camera will aim for 18% gray independently, meaning if you take a photo of an entirely white surface, and an entirely black surface you should get two identical images which both are gray (at least in theory). Thus enters the Macbeth chart.

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Note that Chroma Key Green is reasonably close to an 18% gray reflectance.

http://www.rags-int-inc.com/PhotoTechStuff/MacbethTarget/

https://upload.wikimedia.org/wikipedia/commons/b/b4/CIE1931xy_ColorChecker_SMIL.svg

RGB coordinates of the Macbeth ColorChecker

https://pdfs.semanticscholar.org/0e03/251ad1e6d3c3fb9cb0b1f9754351a959e065.pdf

1: Introduction Title: Managing a VFX Facility’s Render Wall

• Briefly introduce the importance of managing a VFX facility’s render wall.
• Highlight how efficient management contributes to project timelines and overall productivity.

2: Daily Overview Title: Daily Management Routine

• Monitor Queues: Begin each day by reviewing render queues to assess workload and priorities.
• Resource Allocation: Allocate resources based on project demands and available hardware.
• Job Prioritization: Set rendering priorities according to project deadlines and importance.
• Queue Optimization: Adjust queue settings to maximize rendering efficiency.

3: Resource Allocation Title: Efficient Resource Management

• Hardware Utilization: Distribute rendering tasks across available machines for optimal resource usage.
• Balance Workloads: Avoid overloading specific machines while others remain underutilized.
• Consider Off-Peak Times: Schedule resource-intensive tasks during off-peak hours to enhance overall performance.

4: Job Prioritization Title: Prioritizing Rendering Tasks

• Deadline Sensitivity: Give higher priority to tasks with imminent deadlines to ensure timely delivery.
• Critical Shots: Identify shots crucial to the project’s narrative or visual impact for prioritization.
• Dependent Shots: Sequence shots that depend on others should be prioritized together.

5: Queue Optimization and Reporting Title: Streamlining Render Queues

• Dependency Management: Set up dependencies to ensure shots are rendered in the correct order.
• Error Handling: Implement automated error detection and requeueing mechanisms.
• Progress Tracking: Regularly monitor rendering progress and update stakeholders.
• Data Management: Archive completed renders and remove redundant data to free up storage.
• Reporting: Provide daily reports on rendering status, resource usage, and potential bottlenecks.

6: Conclusion Title: Enhancing VFX Workflow

• Effective management of a VFX facility’s render wall is essential for project success.
• Daily monitoring, resource allocation, job prioritization, queue optimization, and reporting are key components.
• A well-managed render wall ensures efficient production, timely delivery, and overall project success.

The terms 16-bit, 16-bit float, and 32-bit refer to different data formats used to store and represent image information, as bits per pixel.

https://en.wikipedia.org/wiki/Color_depth

In color technology, color depth also known as bit depth, is either the number of bits used to indicate the color of a single pixel, OR the number of bits used for each color component of a single pixel.

When referring to a pixel, the concept can be defined as bits per pixel (bpp).

When referring to a color component, the concept can be defined as bits per component, bits per channel, bits per color (all three abbreviated bpc), and also bits per pixel component, bits per color channel or bits per sample (bps). Modern standards tend to use bits per component, but historical lower-depth systems used bits per pixel more often.

Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed; the other aspect is how broad a range of colors can be expressed (the gamut). The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a digital code value to a location in a color space.

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https://waxy.org/2022/09/ai-data-laundering-how-academic-and-nonprofit-researchers-shield-tech-companies-from-accountability/

“Simon Willison created a Datasette browser to explore WebVid-10M, one of the two datasets used to train the video generation model, and quickly learned that all 10.7 million video clips were scraped from Shutterstock, watermarks and all.”

“In addition to the Shutterstock clips, Meta also used 10 million video clips from this 100M video dataset from Microsoft Research Asia. It’s not mentioned on their GitHub, but if you dig into the paper, you learn that every clip came from over 3 million YouTube videos.”

“It’s become standard practice for technology companies working with AI to commercially use datasets and models collected and trained by non-commercial research entities like universities or non-profits.”

“Like with the artists, photographers, and other creators found in the 2.3 billion images that trained Stable Diffusion, I can’t help but wonder how the creators of those 3 million YouTube videos feel about Meta using their work to train their new model.”

How To Make A Blockbuster Movie Trailer

More references in the post

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nofilmschool.com/types-of-film-lights

“Not every light performs the same way. Lights and lighting are tricky to handle. You have to plan for every circumstance. But the good news is, lighting can be adjusted. Let’s look at different factors that affect lighting in every scene you shoot. ”

Use CRI, Luminous Efficacy and color temperature controls to match your needs.

Color Temperature
Color temperature describes the “color” of white light by a light source radiated by a perfect black body at a given temperature measured in degrees Kelvin

https://www.pixelsham.com/2019/10/18/color-temperature/

CRI
“The Color Rendering Index is a measurement of how faithfully a light source reveals the colors of whatever it illuminates, it describes the ability of a light source to reveal the color of an object, as compared to the color a natural light source would provide. The highest possible CRI is 100. A CRI of 100 generally refers to a perfect black body, like a tungsten light source or the sun. ”

https://www.studiobinder.com/blog/what-is-color-rendering-index/

https://en.wikipedia.org/wiki/Color_rendering_index

Luminous Efficacy
Luminous efficacy is a measure of how well a light source produces visible light, watts out versus watts in, measured in lumens per watt. In other words it is a measurement that indicates the ability of a light source to emit visible light using a given amount of power. It is a ratio of the visible energy to the power that goes into the bulb.

FILM LIGHT TYPES

https://www.studiobinder.com/blog/video-lighting-kits/?utm_campaign=Weekly_Newsletter&utm_medium=email&utm_source=sendgrid&utm_term=production-lighting&utm_content=production-lighting

Consumer light types

https://www.researchgate.net/figure/Emission-spectra-of-different-light-sources-a-incandescent-tungsten-light-bulb-b_fig1_312320039

http://dev.informationdisplay.org/IDArchive/2015/NovemberDecember/FrontlineTechnologyCandleLikeEmission.aspx

Tungsten Lights
Light interiors and match domestic places or office locations. Daylight.

Advantages of Tungsten Lights
Almost perfect color rendition
Low cost
Does not use mercury like CFLs (fluorescent) or mercury vapor lights
Better color temperature than standard tungsten
Longer life than a conventional incandescent
Instant on to full brightness, no warm-up time, and it is dimmable

Disadvantages of Tungsten Lights
Extremely hot
High power requirement
The lamp is sensitive to oils and cannot be touched
The bulb is capable of blowing and sending hot glass shards outward. A screen or layer of glass on the outside of the lamp can protect users.

Hydrargyrum medium-arc iodide lights
HMI’s are used when high output is required. They are also used to recreate sun shining through windows or to fake additional sun while shooting exteriors. HMIs can light huge areas at once.

Advantages of HMI lights
High light output
Higher efficiency
High color temperature

Disadvantages of HMI lights:
High cost
High power requirement
Dims only to about 50%
the color temperature increases with dimming
HMI bulbs will explode is dropped and release toxic chemicals

Fluorescent
Fluorescent film lighting is achieved by laying multiple tubes next to each other, combining as many as you want for the desired brightness. The good news is you can choose your bulbs to either be warm or cool depending on the scenario you’re shooting. You want to get these bulbs close to the subject because they’re not great at opening up spaces. Fluorescent lighting is used to light interiors and is more compact and cooler than tungsten or HMI lighting.

Advantages of Fluorescent lights
High efficiency
Low power requirement
Low cost
Long lamp life
Cool
Capable of soft even lighting over a large area
Lightweight

Disadvantages of Fluorescent lights
Flicker
High CRI
Domestic tubes have low CRI & poor color rendition.

LED
LED’s are more and more common on film sets. You can use batteries to power them. That makes them portable and sleek – no messy cabled needed. You can rig your own panels of LED lights to fit any space necessary as well. LED’s can also power Fresnel style lamp heads such as the Arri L-series.

Advantages of LED light
Soft, even lighting
Pure light without UV-artifacts
High efficiency
Low power consumption, can be battery powered
Excellent dimming by means of pulse width modulation control
Long lifespan
Environmentally friendly
Insensitive to shock
No risk of explosion

Disadvantages of LED light
High cost.
LED’s are currently still expensive for their total light output

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https://www.freecodecamp.org/news/advanced-computer-vision-with-python/

https://www.freecodecamp.org/news/how-to-use-opencv-and-python-for-computer-vision-and-ai/

Working for a VFX (Visual Effects) studio provides numerous opportunities to leverage the power of Python and OpenCV for various tasks. OpenCV is a versatile computer vision library that can be applied to many aspects of the VFX pipeline. Here’s a detailed list of opportunities to take advantage of Python and OpenCV in a VFX studio:

1. Image and Video Processing:
   • Preprocessing: Python and OpenCV can be used for tasks like resizing, color correction, noise reduction, and frame interpolation to prepare images and videos for further processing.
   • Format Conversion: Convert between different image and video formats using OpenCV’s capabilities.
2. Tracking and Matchmoving:
   • Feature Detection and Tracking: Utilize OpenCV to detect and track features in image sequences, which is essential for matchmoving tasks to integrate computer-generated elements into live-action footage.
3. Rotoscoping and Masking:
   • Segmentation and Masking: Use OpenCV for creating and manipulating masks and alpha channels for various VFX tasks, like isolating objects or characters from their backgrounds.
4. Camera Calibration:
   • Intrinsic and Extrinsic Calibration: Python and OpenCV can help calibrate cameras for accurate 3D scene reconstruction and camera tracking.
5. 3D Scene Reconstruction:
   • Stereoscopy: Use OpenCV to process stereoscopic image pairs for creating 3D depth maps and generating realistic 3D scenes.
   • Structure from Motion (SfM): Implement SfM techniques to create 3D models from 2D image sequences.
6. Green Screen and Blue Screen Keying:
   • Chroma Keying: Implement advanced keying algorithms using OpenCV to seamlessly integrate actors and objects into virtual environments.
7. Particle and Fluid Simulations:
   • Particle Tracking: Utilize OpenCV to track and manipulate particles in fluid simulations for more realistic visual effects.
8. Motion Analysis:
   • Optical Flow: Implement optical flow algorithms to analyze motion patterns in footage, useful for creating dynamic VFX elements that follow the motion of objects.
9. Virtual Set Extension:
   • Camera Projection: Use camera calibration techniques to project virtual environments onto physical sets, extending the visual scope of a scene.
10. Color Grading:
    • Color Correction: Implement custom color grading algorithms to match the color tones and moods of different shots.
11. Automated QC (Quality Control):
    • Artifact Detection: Develop Python scripts to automatically detect and flag visual artifacts like noise, flicker, or compression artifacts in rendered frames.
12. Data Analysis and Visualization:
    • Performance Metrics: Use Python to analyze rendering times and optimize the rendering process.
    • Data Visualization: Generate graphs and charts to visualize render farm usage, project progress, and resource allocation.
13. Automating Repetitive Tasks:
    • Batch Processing: Automate repetitive tasks like resizing images, applying filters, or converting file formats across multiple shots.
14. Machine Learning Integration:
    • Object Detection: Integrate machine learning models (using frameworks like TensorFlow or PyTorch) to detect and track specific objects or elements within scenes.
15. Pipeline Integration:
    • Custom Tools: Develop Python scripts and tools to integrate OpenCV-based processes seamlessly into the studio’s pipeline.
16. Real-time Visualization:
    • Live Previsualization: Implement real-time OpenCV-based visualizations to aid decision-making during the preproduction stage.
17. VR and AR Integration:
    • Augmented Reality: Use Python and OpenCV to integrate virtual elements into real-world footage, creating compelling AR experiences.
18. Camera Effects:
    • Lens Distortion: Correct lens distortions and apply various camera effects using OpenCV, contributing to the desired visual style.

Interpolating frames from an EXR sequence using OpenCV can be useful when you have only every second frame of a final render and you want to create smoother motion by generating intermediate frames. However, keep in mind that interpolating frames might not always yield perfect results, especially if there are complex changes between frames. Here’s a basic example of how you might use OpenCV to achieve this:

import cv2
import numpy as np
import os

# Replace with the path to your EXR frames
exr_folder = "path_to_exr_frames"

# Replace with the appropriate frame extension and naming convention
frame_template = "frame_{:04d}.exr"

# Define the range of frame numbers you have
start_frame = 1
end_frame = 100
step = 2

# Define the output folder for interpolated frames
output_folder = "output_interpolated_frames"
os.makedirs(output_folder, exist_ok=True)

# Loop through the frame range and interpolate
for frame_num in range(start_frame, end_frame + 1, step):
    frame_path = os.path.join(exr_folder, frame_template.format(frame_num))
    next_frame_path = os.path.join(exr_folder, frame_template.format(frame_num + step))

    if os.path.exists(frame_path) and os.path.exists(next_frame_path):
        frame = cv2.imread(frame_path, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)
        next_frame = cv2.imread(next_frame_path, cv2.IMREAD_ANYDEPTH | cv2.IMREAD_COLOR)

        # Interpolate frames using simple averaging
        interpolated_frame = (frame + next_frame) / 2

        # Save interpolated frame
        output_path = os.path.join(output_folder, frame_template.format(frame_num))
        cv2.imwrite(output_path, interpolated_frame)

        print(f"Interpolated frame {frame_num}") # alternatively: print("Interpolated frame {}".format(frame_num))

Please note the following points:

• The above example uses simple averaging to interpolate frames. More advanced interpolation methods might provide better results, such as motion-based algorithms like optical flow-based interpolation.
• EXR files can store high dynamic range (HDR) data, so make sure to use cv2.IMREAD_ANYDEPTH flag when reading these files.
• OpenCV might not support EXR format directly. You might need to use a library like exr to read and manipulate EXR files, and then convert them to OpenCV-compatible formats.
• Consider the characteristics of your specific render when using interpolation. If there are large changes between frames, the interpolation might lead to artifacts.
• Experiment with different interpolation methods and parameters to achieve the desired result.
• For a more advanced and accurate interpolation, you might need to implement or use existing algorithms that take into account motion estimation and compensation.

www.videomaker.com/article/c03/18984-how-to-calculate-contrast-ratios-for-more-professional-lighting-setups

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

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rawtherapee.com/

5.10 of this tool includes excellent tools to clean up cr2 and cr3 used on set to support HDRI processing.
Converting raw to AcesCG 32 bit tiffs with metadata. 

www.andreageremia.it/tutorial_python_tcl.html

https://www.gatimedia.co.uk/list-of-knobs-2

https://learn.foundry.com/nuke/developers/63/ndkdevguide/knobs-and-handles/knobtypes.html

http://www.andreageremia.it/tutorial_python_tcl.html

http://thoughtvfx.blogspot.com/2012/12/nuke-tcl-tips.html

Check final image quality
https://www.compositingpro.com/tech-check-compositing-shot-in-nuke/

Local copy:
http://pixelsham.com/wp-content/uploads/2023/03/compositing_pro_tech_check_nuke_script.nk

Nuke tcl procedures
https://www.gatimedia.co.uk/nuke-tcl-procedures

Knobs
https://learn.foundry.com/nuke/developers/63/ndkdevguide/knobs-and-handles/knobtypes.html

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Also see: https://www.pixelsham.com/2015/05/16/how-aperture-shutter-speed-and-iso-affect-your-photos/

In photography, exposure value (EV) is a number that represents a combination of a camera’s shutter speed and f-number, such that all combinations that yield the same exposure have the same EV (for any fixed scene luminance).

The EV concept was developed in an attempt to simplify choosing among combinations of equivalent camera settings. Although all camera settings with the same EV nominally give the same exposure, they do not necessarily give the same picture. EV is also used to indicate an interval on the photographic exposure scale. 1 EV corresponding to a standard power-of-2 exposure step, commonly referred to as a stop

EV 0 corresponds to an exposure time of 1 sec and a relative aperture of f/1.0. If the EV is known, it can be used to select combinations of exposure time and f-number.

https://www.streetdirectory.com/travel_guide/141307/photography/exposure_value_ev_and_exposure_compensation.html

Note EV does not equal to photographic exposure. Photographic Exposure is defined as how much light hits the camera’s sensor. It depends on the camera settings mainly aperture and shutter speed. Exposure value (known as EV) is a number that represents the exposure setting of the camera.

Thus, strictly, EV is not a measure of luminance (indirect or reflected exposure) or illuminance (incidental exposure); rather, an EV corresponds to a luminance (or illuminance) for which a camera with a given ISO speed would use the indicated EV to obtain the nominally correct exposure. Nonetheless, it is common practice among photographic equipment manufacturers to express luminance in EV for ISO 100 speed, as when specifying metering range or autofocus sensitivity.

The exposure depends on two things: how much light gets through the lenses to the camera’s sensor and for how long the sensor is exposed. The former is a function of the aperture value while the latter is a function of the shutter speed. Exposure value is a number that represents this potential amount of light that could hit the sensor. It is important to understand that exposure value is a measure of how exposed the sensor is to light and not a measure of how much light actually hits the sensor. The exposure value is independent of how lit the scene is. For example a pair of aperture value and shutter speed represents the same exposure value both if the camera is used during a very bright day or during a dark night.

Each exposure value number represents all the possible shutter and aperture settings that result in the same exposure. Although the exposure value is the same for different combinations of aperture values and shutter speeds the resulting photo can be very different (the aperture controls the depth of field while shutter speed controls how much motion is captured).

EV 0.0 is defined as the exposure when setting the aperture to f-number 1.0 and the shutter speed to 1 second. All other exposure values are relative to that number. Exposure values are on a base two logarithmic scale. This means that every single step of EV – plus or minus 1 – represents the exposure (actual light that hits the sensor) being halved or doubled.

https://www.streetdirectory.com/travel_guide/141307/photography/exposure_value_ev_and_exposure_compensation.html

Formula

https://en.wikipedia.org/wiki/Exposure_value

https://www.scantips.com/lights/math.html

which means   2^EV = N² / t

where

• N is the relative aperture (f-number) Important: Note that f/stop values must first be squared in most calculations
• t is the exposure time (shutter speed) in seconds

EV 0 corresponds to an exposure time of 1 sec and an aperture of f/1.0.

Example: If f/16 and 1/4 second, then this is:

(N² / t) = (16 × 16 ÷ 1/4) = (16 × 16 × 4) = 1024.

Log₂(1024) is EV 10. Meaning, 2¹⁰ = 1024.

Collecting photographic exposure using Light Meters

https://photo.stackexchange.com/questions/968/how-can-i-correctly-measure-light-using-a-built-in-camera-meter

The exposure meter in the camera does not know whether the subject itself is bright or not. It simply measures the amount of light that comes in, and makes a guess based on that. The camera will aim for 18% gray, meaning if you take a photo of an entirely white surface, and an entirely black surface you should get two identical images which both are gray (at least in theory)

https://en.wikipedia.org/wiki/Light_meter

For reflected-light meters, camera settings are related to ISO speed and subject luminance by the reflected-light exposure equation:

where

• N is the relative aperture (f-number)
• t is the exposure time (“shutter speed”) in seconds
• L is the average scene luminance
• S is the ISO arithmetic speed
• K is the reflected-light meter calibration constant

For incident-light meters, camera settings are related to ISO speed and subject illuminance by the incident-light exposure equation:

where

• E is the illuminance (in lux)
• C is the incident-light meter calibration constant

Two values for K are in common use: 12.5 (Canon, Nikon, and Sekonic) and 14 (Minolta, Kenko, and Pentax); the difference between the two values is approximately 1/6 EV.
For C a value of 250 is commonly used.

Nonetheless, it is common practice among photographic equipment manufacturers to also express luminance in EV for ISO 100 speed. Using K = 12.5, the relationship between EV at ISO 100 and luminance L is then :

L = 2^(EV-3)

The situation with incident-light meters is more complicated than that for reflected-light meters, because the calibration constant C depends on the sensor type. Illuminance is measured with a flat sensor; a typical value for C is 250 with illuminance in lux. Using C = 250, the relationship between EV at ISO 100 and illuminance E is then :

E = 2.5 * 2^(EV)

https://nofilmschool.com/2018/03/want-easier-and-faster-way-calculate-exposure-formula

Three basic factors go into the exposure formula itself instead: aperture, shutter, and ISO. Plus a light meter calibration constant.

f-stop²/shutter (in seconds) = lux * ISO/C

If you at least know four of those variables, you’ll be able to calculate the missing value.

So, say you want to figure out how much light you’re going to need in order to shoot at a certain f-stop. Well, all you do is plug in your values (you should know the f-stop, ISO, and your light meter calibration constant) into the formula below:

lux = C (f-stop²/shutter (in seconds))/ISO

Exposure Value Calculator:

https://snapheadshots.com/resources/exposure-and-light-calculator

https://www.scantips.com/lights/exposurecalc.html

https://www.pointsinfocus.com/tools/exposure-settings-ev-calculator/#google_vignette

From that perspective, an exposure stop is a measurement of Exposure and provides a universal linear scale to measure the increase and decrease in light, exposed to the image sensor, due to changes in shutter speed, iso & f-stop.
+-1 stop is a doubling or halving of the amount of light let in when taking a photo.
1 EV is just another way to say one stop of exposure change.

One major use of EV (Exposure Value) is just to measure any change of exposure, where one EV implies a change of one stop of exposure. Like when we compensate our picture in the camera.

If the picture comes out too dark, our manual exposure could correct the next one by directly adjusting one of the three exposure controls (f/stop, shutter speed, or ISO). Or if using camera automation, the camera meter is controlling it, but we might apply +1 EV exposure compensation (or +1 EV flash compensation) to make the result goal brighter, as desired. This use of 1 EV is just another way to say one stop of exposure change.

On a perfect day the difference from sampling the sky vs the sun exposure with diffusing spot meters is about 3.2 exposure difference.

 ~15.4 EV for the sun
 ~12.2 EV for the sky

That is as a ballpark. All still influenced by surroundings, accuracy parameters, fov of the sensor…

EV calculator

https://www.scantips.com/lights/evchart.html#calc

http://www.fredparker.com/ultexp1.htm

Exposure value is basically used to indicate an interval on the photographic exposure scale, with a difference of 1 EV corresponding to a standard power-of-2 exposure step, also commonly referred to as a “stop”.

https://contrastly.com/a-guide-to-understanding-exposure-value-ev/

Retrieving photographic exposure from an image

IF you are still trying to gauge relative brightness, the level of the sun in Nuke can vary, but it should be in the thousands. Ie: between 30,000 and 65,0000 rgb value depending on time of the day, season and atmospherics.

The values for a 12 o’clock sun, with the sun sampled at EV 15.5 (shutter 1/30, ISO 100, F22) is 32.000 RGB max values (or 32,000 pixel luminance).
The thing to keep an eye for is the level of contrast between sunny side/fill side.  The terminator should be quite obvious,  there can be up to 3 stops difference between fill/key in sunny lit objects.

Note: In Foundry’s Nuke, the software will map 18% gray to whatever your center f/stop is set to in the viewer settings (f/8 by default… change that to EV by following the instructions below).
You can experiment with this by attaching an Exposure node to a Constant set to 0.18, setting your viewer read-out to Spotmeter, and adjusting the stops in the node up and down. You will see that a full stop up or down will give you the respective next value on the aperture scale (f8, f11, f16 etc.).
One stop doubles or halves the amount or light that hits the filmback/ccd, so everything works in powers of 2.
So starting with 0.18 in your constant, you will see that raising it by a stop will give you .36 as a floating point number (in linear space), while your f/stop will be f/11 and so on.

If you set your center stop to 0 (see below) you will get a relative readout in EVs, where EV 0 again equals 18% constant gray.
Note: make sure to set your Nuke read node to ‘raw data’

In other words. Setting the center f-stop to 0 means that in a neutral plate, the middle gray in the macbeth chart will equal to exposure value 0. EV 0 corresponds to an exposure time of 1 sec and an aperture of f/1.0.

To switch Foundry’s Nuke’s SpotMeter to return the EV of an image, click on the main viewport, and then press s, this opens the viewer’s properties. Now set the center f-stop to 0 in there. And the SpotMeter in the viewport will change from aperture and fstops to EV.

If you are trying to gauge the EV from the pixel luminance in the image:
– Setting the center f-stop to 0 means that in a neutral plate, the middle 18% gray will equal to exposure value 0.
– So if EV 0 = 0.18 middle gray in nuke which equal to a pixel luminance of 0.18, doubling that value, doubles the EV.

.18 pixel luminance = 0EV
.36 pixel luminance = 1EV
.72 pixel luminance = 2EV
1.46 pixel luminance = 3EV
...

This is a Geometric Progression function: x_n = ar^(n-1)

The most basic example of this function is 1,2,4,8,16,32,… The sequence starts at 1 and doubles each time, so

• a=1 (the first term)
• r=2 (the “common ratio” between terms is a doubling)

And we get:

{a, ar, ar², ar³, … }

= {1, 1×2, 1×2², 1×2³, … }

= {1, 2, 4, 8, … }

In this example the function translates to: n = 2^(n-1)
You can graph this curve through this expression: x = 2^(y-1)  :

You can go back and forth between the two values through a geometric progression function and a log function:

(Note: in a spreadsheet this is: = POWER(2; cell# -1)  and  =LOG(cell#, 2)+1) )

Translating this into a geometric progression between an image pixel luminance and EV:

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www.hdrlabs.com/picturenaut/plugins.html

Note. The Median Cut algorithm is typically used for color quantization, which involves reducing the number of colors in an image while preserving its visual quality. It doesn’t directly provide a way to identify the brightest areas in an image. However, if you’re interested in identifying the brightest areas, you might want to look into other methods like thresholding, histogram analysis, or edge detection, through openCV for example.

Here is an openCV example:

# bottom left coordinates = 0,0
import numpy as np
import cv2

# Load the HDR or EXR image
image = cv2.imread('your_image_path.exr', cv2.IMREAD_UNCHANGED)  # Load as-is without modification

# Calculate the luminance from the HDR channels (assuming RGB format)
luminance = np.dot(image[..., :3], [0.299, 0.587, 0.114])

# Set a threshold value based on estimated EV
threshold_value = 2.4  # Estimated threshold value based on 4.8 EV

# Apply the threshold to identify bright areas
# The luminance array contains the calculated luminance values for each pixel in the image. # The threshold_value is a user-defined value that represents a cutoff point, separating "bright" and "dark" areas in terms of perceived luminance.

thresholded = (luminance > threshold_value) * 255 

# Convert the thresholded image to uint8 for contour detection 
thresholded = thresholded.astype(np.uint8) 

# Find contours of the bright areas 
contours, _ = cv2.findContours(thresholded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) 

# Create a list to store the bounding boxes of bright areas 
bright_areas = [] 

# Iterate through contours and extract bounding boxes for contour in contours: 
x, y, w, h = cv2.boundingRect(contour) 

# Adjust y-coordinate based on bottom-left origin 
y_bottom_left_origin = image.shape[0] - (y + h) bright_areas.append((x, y_bottom_left_origin, x + w, y_bottom_left_origin + h)) 

# Store as (x1, y1, x2, y2) 
# Print the identified bright areas 
print("Bright Areas (x1, y1, x2, y2):") for area in bright_areas: print(area)

More details

Luminance and Exposure in an EXR Image:

• An EXR (Extended Dynamic Range) image format is often used to store high dynamic range (HDR) images that contain a wide range of luminance values, capturing both dark and bright areas.
• Luminance refers to the perceived brightness of a pixel in an image. In an RGB image, luminance is often calculated using a weighted sum of the red, green, and blue channels, where different weights are assigned to each channel to account for human perception.
• In an EXR image, the pixel values can represent radiometrically accurate scene values, including actual radiance or irradiance levels. These values are directly related to the amount of light emitted or reflected by objects in the scene.

The luminance line is calculating the luminance of each pixel in the image using a weighted sum of the red, green, and blue channels. The three float values [0.299, 0.587, 0.114] are the weights used to perform this calculation.

These weights are based on the concept of luminosity, which aims to approximate the perceived brightness of a color by taking into account the human eye’s sensitivity to different colors. The values are often derived from the NTSC (National Television System Committee) standard, which is used in various color image processing operations.

Here’s the breakdown of the float values:

• 0.299: Weight for the red channel.
• 0.587: Weight for the green channel.
• 0.114: Weight for the blue channel.

The weighted sum of these channels helps create a grayscale image where the pixel values represent the perceived brightness. This technique is often used when converting a color image to grayscale or when calculating luminance for certain operations, as it takes into account the human eye’s sensitivity to different colors.

For the threshold, remember that the exact relationship between EV values and pixel values can depend on the tone-mapping or normalization applied to the HDR image, as well as the dynamic range of the image itself.

To establish a relationship between exposure and the threshold value, you can consider the relationship between linear and logarithmic scales:

1. Linear and Logarithmic Scales:
   • Exposure values in an EXR image are often represented in logarithmic scales, such as EV (exposure value). Each increment in EV represents a doubling or halving of the amount of light captured.
   • Threshold values for luminance thresholding are usually linear, representing an actual luminance level.
2. Conversion Between Scales:
   • To establish a mathematical relationship, you need to convert between the logarithmic exposure scale and the linear threshold scale.
   • One common method is to use a power function. For instance, you can use a power function to convert EV to a linear intensity value.
   threshold_value = base_value * (2 ** EV)


   Here, EV is the exposure value, base_value is a scaling factor that determines the relationship between EV and threshold_value, and 2 ** EV is used to convert the logarithmic EV to a linear intensity value.

3. Choosing the Base Value:
   • The base_value factor should be determined based on the dynamic range of your EXR image and the specific luminance values you are dealing with.
   • You may need to experiment with different values of base_value to achieve the desired separation of bright areas from the rest of the image.

Let’s say you have an EXR image with a dynamic range of 12 EV, which is a common range for many high dynamic range images. In this case, you want to set a threshold value that corresponds to a certain number of EV above the middle gray level (which is often considered to be around 0.18).

Here’s an example of how you might determine a base_value to achieve this:

# Define the dynamic range of the image in EV
dynamic_range = 12

# Choose the desired number of EV above middle gray for thresholding
desired_ev_above_middle_gray = 2

# Calculate the threshold value based on the desired EV above middle gray
threshold_value = 0.18 * (2 ** (desired_ev_above_middle_gray / dynamic_range))

print("Threshold Value:", threshold_value)

Chris is now using Google’s Looker Studio (this may better help those that aren’t able to use the filters on the spreadsheet):

https://lookerstudio.google.com/u/0/reporting/2f39b56e-7393-4aa2-9fd5-bf8bf615c95f/page/5koHB

Older format: docs.google.com/spreadsheets/d/1eR2oAXOuflr8CZeGoz3JTrsgNj3KuefbdXJOmNtjEVM/edit#gid=0

For any studios that would like to add positions to this, please feel free to use the following form:
https://docs.google.com/forms/d/e/1FAIpQLSeXziY3GQ8N7bxM-GxwDoZ7AimguHru0105PLVQtNYygswIlw/viewform

www.cartoonbrew.com/interviews/this-spreadsheet-is-probably-the-best-job-board-in-the-animation-industry-and-its-run-by-one-person-in-his-spare-time-185646.html

The 300dpi digital poster is now available to all PixelSham.com subscribers.

If you have already subscribed and wish a copy, please send me a note through the contact page.

https://www.copypastecharacter.com

Most used ones:

Alt + 0149   •  bullet point
Alt + 0153   ™  trademark symbol
Alt + 0169  ©  copyright symbol
Alt + 0174  ®  registered ­ trademark symbol
Alt + 0176  °  degree symbol
Alt + 0177   ±  plus-or-minus sign
Alt + 0215  ×  multi­plication sign
Alt + 12  ♀  female sign
Alt + 11  ♂  m­ale sign
Alt + 13  ♪  e­ighth note
Alt + 14  ♫  ­beamed eighth note
Alt + 251  √  square root check mark
Alt + 8236  ∞   ­infinity
Alt + 24  ↑  up arrow
Alt + 25  ↓  down arrow
Alt + 26  →  ri­ght arrow
Alt + 27  ←  l­eft arrow
Alt + 29  ↔  lef­t right arrow

All of them:

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(more…)

https://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
(more…)

https://www.animationxpress.com/vfx/meet-daniele-tosti-a-senior-cg-artist-who-is-on-a-mission-to-inspire-the-next-generation-of-artists/

You’ve been in the VFX Industry for over a decade. Tell us about your journey.

It all started with my older brother giving me a Commodore64 personal computer as a gift back in the late 80′. I realised then I could create something directly from my imagination using this new digital media format. And, eventually, make a living in the process.
That led me to start my professional career in 1990. From live TV to games to animation. All the way to live action VFX in the recent years.

I really never stopped to crave to create art since those early days. And I have been incredibly fortunate to work with really great talent along the way, which made my journey so much more effective.

What inspired you to pursue VFX as a career?

An incredible combination of opportunities, really. The opportunity to express myself as an artist and earn money in the process. The opportunity to learn about how the world around us works and how best solve problems. The opportunity to share my time with other talented people with similar passions. The opportunity to grow and adapt to new challenges. The opportunity to develop something that was never done before. A perfect storm of creativity that fed my continuous curiosity about life and genuinely drove my inspiration.

Tell us about the projects you’ve particularly enjoyed working on in your career

I quite enjoyed working on live TV projects, as the combination of tight deadlines and high quality was quite an incredible learning platform as a professional artist. But working on large, high end live action feature projects was really where I learnt most of my trade. And gave me the most satisfaction.

Every film I worked on had some memorable experiences. Right from Avatar to Iron Man 3 to Jungle Book to The Planet of the Apes to The Hobbits to name a few.

But above all, the technical challenges and the high quality we reached in each and every of the projects that I worked on, the best memories come from working with amazing and skilled artists, from a variety of disciplines. As those were my true mentors and became my best friends.

Post Production, Animation, VFX, Motion Graphics, Video Editing …

What are some technologies and trends that you think are emerging in the VFX Industry?

In the last few years there has definitely been a bias from some major studios to make VFX a commodity. In the more negative sense of the word. When any product reaches a level of quality that attracts a mass of consumers and reaches a plateau of opportunities, large corporation tend to respond with maximising its sale values by leveraging marketing schemes and deliverable more than the core values of the product itself. This is often a commoditisation approach that tends to empower agents who are not necessarily knowledgeable of a product’s cycles, and in that process, lowering the quality of the product itself for the sake of profits. It is a pretty common event in modern society and it applies to any brand name, not just VFX.

One challenge with VFX’s technology and artistry is that it relies on the effectiveness of artists and visionaries for the most. And limiting the authority, ownerships and perspective of such a crowd has definitely directly impacted the overall quality of the last decade of productions, both technically and artistically. There are very few and apart creative forces who have been able to deliver project that one could identify as a truly creative breakthrough. While the majority of productions seem to have suffered from some of these commoditisation patterns.

The other bigger challenge with this current trend is that VFX, due to various, historical business arrangements, is often relying on unbalanced resources as well as very small and feeble economic cycles and margins. Which make the entire industry extremely susceptible to marketing failures and to unstable leadership. As a few recent bankruptcies have demonstrated.

It is taking some reasonable time for the VFX crowd to acknowledge these trends and learn to be profitable, as the majority has never been educated on fair business practices.

But. Thankfully, the VFX circle is also a crowd of extremely adaptable and talented individuals, who are quite capable at resolving issues, finding alternatives and leveraging their passion. Which I believe is one of the drives behind the current evolution in the use of artificial intelligence, virtual reality, virtual production, real time rendering, and so on.

There is still a long path ahead of us but I hope we are all learning ways to make our passion speaks in profitable ways for everyone.

It is also highly likely that, in a near future, larger software and hardware corporation, thanks to their more profitable business practices, large development teams and better understanding of marketing, will eventually take over a lot of the cycles that the current production houses currently run. And in that process allow creative studios to focus back on VFX artistry.

What effect has the pandemics-induced lockdown had on the industry?

It is still early to say. I fear that if live action production does not start soon, we may see some of the economic challenges I mention above. At both studio and artists’ scale. There is definitely a push from production houses to make large distribution clients understand the fragility of the moment, especially in relation to payment cycles and economic support. Thus, there is still a fair risk that the few studios which adopted a more commoditised view to production will make their artists pay some price for their choices.

But, any challenge brings opportunities. For example, there is finally some recognition into a momentum to rely on work-from-home as a feasible solution to a lot of the current office production’s limitations and general artistry restrictions. Which, while there is no win-win in this pandemic, could be a silver lining.

What would you say to the budding artists who wish to become CG artists or VFX professionals?

Follow your passion but treat this career as any other business.
Learn to be adaptable. Find a true balance between professional and family life. Carefully plan your future. And watch our channel to learn more about all these.

What inspired you to create a channel for aspiring artists?

As many fellow and respected artists, I love this industry, but I had to understand a lot of business practices at my own expenses.
You can learn tools, cycles and software from books and schools. But production life tends to drive its own rhythms and there are fewer opportunities to absorb those.

Along my career I had some challenges finding professional willing to share their time to invest into me. But I was still extremely fortunate to find other mentors who helped me to be economically and professionally successful in this business. I owe a lot to these people. I promised myself I would exchange that favour by helping other artists, myself.

What can students expect to learn from your channel?

I am excited to have the opportunity to fill some of the voids that the current education systems and industry may have. This by helping new artists with true life stories by some of the most accomplished and successful talents I met during my career. We will talk about technology trends as much as our life experiences as artists. Discussing career advises. Trying to look into the future of the industry. And suggesting professional tips. The aim through this mentor-ship is to inspire new generations to focus on what is more important for the VFX industry. Take responsibilities for their art and passions as much as their families.

And, in the process, to feel empowered to materialise from their imagination more and more of those creative, awe inspiring moments that this art form has gifted us with so far.

http://TheCGCareer.com

https://www.urtech.ca/2019/04/solved-complete-list-of-screen-resolution-names-sizes-and-aspect-ratios/

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http://www.calculator.org/property.aspx?name=solid+angle

A measure of how large the object appears to an observer looking from that point. Thus. A measure for objects in the sky. Useful to retuen the size of the sun and moon… and in perspective, how much of their contribution to lighting. Solid angle can be represented in ‘angular diameter’ as well.

http://en.wikipedia.org/wiki/Solid_angle

http://www.mathsisfun.com/geometry/steradian.html

A solid angle is expressed in a dimensionless unit called a steradian (symbol: sr). By default in terms of the total celestial sphere and before atmospheric’s scattering, the Sun and the Moon subtend fractional areas of 0.000546% (Sun) and 0.000531% (Moon).

http://en.wikipedia.org/wiki/Solid_angle#Sun_and_Moon

On earth the sun is likely closer to 0.00011 solid angle after athmospheric scattering. The sun as perceived from earth has a diameter of 0.53 degrees. This is about 0.000064 solid angle.

http://www.numericana.com/answer/angles.htm

The mean angular diameter of the full moon is 2q = 0.52° (it varies with time around that average, by about 0.009°). This translates into a solid angle of 0.0000647 sr, which means that the whole night sky covers a solid angle roughly one hundred thousand times greater than the full moon.

More info

http://lcogt.net/spacebook/using-angles-describe-positions-and-apparent-sizes-objects

http://amazing-space.stsci.edu/glossary/def.php.s=topic_astronomy

Angular Size

The apparent size of an object as seen by an observer; expressed in units of degrees (of arc), arc minutes, or arc seconds. The moon, as viewed from the Earth, has an angular diameter of one-half a degree.

The angle covered by the diameter of the full moon is about 31 arcmin or 1/2°, so astronomers would say the Moon’s angular diameter is 31 arcmin, or the Moon subtends an angle of 31 arcmin.

I am excited to officially announce the release of a new YouTube channel dedicated to help and support digital artists in the feature production business!

TheCGCareer.com

We will be interviewing some of the most successful senior artists and supervisors in the feature digital art business. This project with the intent of providing artists in the industry with experiences and personal suggestions that can help our careers and success in this art form.

Please, visit us for more information and latest interviews.

en.wikipedia.org/wiki/Go_motion

Petroleum jelly
This crude but reasonably effective technique involves smearing petroleum jelly (“Vaseline”) on a plate of glass in front of the camera lens, also known as vaselensing, then cleaning and reapplying it after each shot — a time-consuming process, but one which creates a blur around the model. This technique was used for the endoskeleton in The Terminator. This process was also employed by Jim Danforth to blur the pterodactyl’s wings in Hammer Films’ When Dinosaurs Ruled the Earth, and by Randal William Cook on the terror dogs sequence in Ghostbusters.[citation needed]

Bumping the puppet
Gently bumping or flicking the puppet before taking the frame will produce a slight blur; however, care must be taken when doing this that the puppet does not move too much or that one does not bump or move props or set pieces.

Moving the table
Moving the table on which the model is standing while the film is being exposed creates a slight, realistic blur. This technique was developed by Ladislas Starevich: when the characters ran, he moved the set in the opposite direction. This is seen in The Little Parade when the ballerina is chased by the devil. Starevich also used this technique on his films The Eyes of the Dragon, The Magical Clock and The Mascot. Aardman Animations used this for the train chase in The Wrong Trousers and again during the lorry chase in A Close Shave. In both cases the cameras were moved physically during a 1-2 second exposure. The technique was revived for the full-length Wallace & Gromit: The Curse of the Were-Rabbit.

Go motion
The most sophisticated technique was originally developed for the film The Empire Strikes Back and used for some shots of the tauntauns and was later used on films like Dragonslayer and is quite different from traditional stop motion. The model is essentially a rod puppet. The rods are attached to motors which are linked to a computer that can record the movements as the model is traditionally animated. When enough movements have been made, the model is reset to its original position, the camera rolls and the model is moved across the table. Because the model is moving during shots, motion blur is created.

A variation of go motion was used in E.T. the Extra-Terrestrial to partially animate the children on their bicycles.

newrepublic.com/article/154944/boeing-737-max-investigation-indonesia-lion-air-ethiopian-airlines-managerial-revolution

Airplane manufacturing is no different from mortgage lending or insulin distribution or make-believe blood analyzing software (or VFX?) —another cash cow for the one percent, bound inexorably for the slaughterhouse.

The beginning of the end was “Boeing’s 1997 acquisition of McDonnell Douglas, a dysfunctional firm with a dilapidated aircraft plant in Long Beach and a CEO (Harry Stonecipher) who liked to use what he called the “Hollywood model” for dealing with engineers: Hire them for a few months when project deadlines are nigh, fire them when you need to make numbers.” And all that came with it. “Stonecipher’s team had driven the last nail in the coffin of McDonnell’s flailing commercial jet business by trying to outsource everything but design, final assembly, and flight testing and sales.”

It is understood, now more than ever, that capitalism does half-assed things like that, especially in concert with computer software and oblivious regulators.

There was something unsettlingly familiar when the world first learned of MCAS in November, about two weeks after the system’s unthinkable stupidity drove the two-month-old plane and all 189 people on it to a horrific death. It smacked of the sort of screwup a 23-year-old intern might have made—and indeed, much of the software on the MAX had been engineered by recent grads of Indian software-coding academies making as little as $9 an hour, part of Boeing management’s endless war on the unions that once represented more than half its employees.

Down in South Carolina, a nonunion Boeing assembly line that opened in 2011 had for years churned out scores of whistle-blower complaints and wrongful termination lawsuits packed with scenes wherein quality-control documents were regularly forged, employees who enforced standards were sabotaged, and planes were routinely delivered to airlines with loose screws, scratched windows, and random debris everywhere.

Shockingly, another piece of the quality failure is Boeing securing investments from all airliners, starting with SouthWest above all, to guarantee Boeing’s production lines support in exchange for fair market prices and favorite treatments. Basically giving Boeing financial stability independently on the quality of their product. “Those partnerships were but one numbers-smoothing mechanism in a diversified tool kit Boeing had assembled over the previous generation for making its complex and volatile business more palatable to Wall Street.”

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https://www.discovery.com/science/mexapixels-in-human-eye

About 576 megapixels for the entire field of view.

Consider a view in front of you that is 90 degrees by 90 degrees, like looking through an open window at a scene. The number of pixels would be:
90 degrees * 60 arc-minutes/degree * 1/0.3 * 90 * 60 * 1/0.3 = 324,000,000 pixels (324 megapixels).

At any one moment, you actually do not perceive that many pixels, but your eye moves around the scene to see all the detail you want. But the human eye really sees a larger field of view, close to 180 degrees. Let’s be conservative and use 120 degrees for the field of view. Then we would see:

120 * 120 * 60 * 60 / (0.3 * 0.3) = 576 megapixels.

Or.

7 megapixels for the 2 degree focus arc… + 1 megapixel for the rest.

https://clarkvision.com/articles/eye-resolution.html

Details in the post

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Also see:

https://www.pixelsham.com/2018/11/22/exposure-value-measurements/

https://www.pixelsham.com/2016/03/03/f-stop-vs-t-stop/

An exposure stop is a unit measurement of Exposure as such it provides a universal linear scale to measure the increase and decrease in light, exposed to the image sensor, due to changes in shutter speed, iso and f-stop.

+-1 stop is a doubling or halving of the amount of light let in when taking a photo

1 EV (exposure value) is just another way to say one stop of exposure change.

https://www.photographymad.com/pages/view/what-is-a-stop-of-exposure-in-photography

Same applies to shutter speed, iso and aperture.
Doubling or halving your shutter speed produces an increase or decrease of 1 stop of exposure.
Doubling or halving your iso speed produces an increase or decrease of 1 stop of exposure.

Details in the post

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RASTERIZATION
Rasterisation (or rasterization) is the task of taking the information described in a vector graphics format OR the vertices of triangles making 3D shapes and converting them into a raster image (a series of pixels, dots or lines, which, when displayed together, create the image which was represented via shapes), or in other words “rasterizing” vectors or 3D models onto a 2D plane for display on a computer screen.

For each triangle of a 3D shape, you project the corners of the triangle on the virtual screen with some math (projective geometry). Then you have the position of the 3 corners of the triangle on the pixel screen. Those 3 points have texture coordinates, so you know where in the texture are the 3 corners. The cost is proportional to the number of triangles, and is only a little bit affected by the screen resolution.

In computer graphics, a raster graphics or bitmap image is a dot matrix data structure that represents a generally rectangular grid of pixels (points of color), viewable via a monitor, paper, or other display medium.

With rasterization, objects on the screen are created from a mesh of virtual triangles, or polygons, that create 3D models of objects. A lot of information is associated with each vertex, including its position in space, as well as information about color, texture and its “normal,” which is used to determine the way the surface of an object is facing.

Computers then convert the triangles of the 3D models into pixels, or dots, on a 2D screen. Each pixel can be assigned an initial color value from the data stored in the triangle vertices.

Further pixel processing or “shading,” including changing pixel color based on how lights in the scene hit the pixel, and applying one or more textures to the pixel, combine to generate the final color applied to a pixel.

The main advantage of rasterization is its speed. However, rasterization is simply the process of computing the mapping from scene geometry to pixels and does not prescribe a particular way to compute the color of those pixels. So it cannot take shading, especially the physical light, into account and it cannot promise to get a photorealistic output. That’s a big limitation of rasterization.

There are also multiple problems:

• If you have two triangles one is behind the other, you will draw twice all the pixels. you only keep the pixel from the triangle that is closer to you (Z-buffer), but you still do the work twice.

• The borders of your triangles are jagged as it is hard to know if a pixel is in the triangle or out. You can do some smoothing on those, that is anti-aliasing.

• You have to handle every triangles (including the ones behind you) and then see that they do not touch the screen at all. (we have techniques to mitigate this where we only look at triangles that are in the field of view)

• Transparency is hard to handle (you can’t just do an average of the color of overlapping transparent triangles, you have to do it in the right order)

RAY CASTING
It is almost the exact reverse of rasterization: you start from the virtual screen instead of the vector or 3D shapes, and you project a ray, starting from each pixel of the screen, until it intersect with a triangle.

The cost is directly correlated to the number of pixels in the screen and you need a really cheap way of finding the first triangle that intersect a ray. In the end, it is more expensive than rasterization but it will, by design, ignore the triangles that are out of the field of view.

You can use it to continue after the first triangle it hit, to take a little bit of the color of the next one, etc… This is useful to handle the border of the triangle cleanly (less jagged) and to handle transparency correctly.

RAYTRACING

Same idea as ray casting except once you hit a triangle you reflect on it and go into a different direction. The number of reflection you allow is the “depth” of your ray tracing. The color of the pixel can be calculated, based off the light source and all the polygons it had to reflect off of to get to that screen pixel.

The easiest way to think of ray tracing is to look around you, right now. The objects you’re seeing are illuminated by beams of light. Now turn that around and follow the path of those beams backwards from your eye to the objects that light interacts with. That’s ray tracing.

Ray tracing is eye-oriented process that needs walking through each pixel looking for what object should be shown there, which is also can be described as a technique that follows a beam of light (in pixels) from a set point and simulates how it reacts when it encounters objects.

Compared with rasterization, ray tracing is hard to be implemented in real time, since even one ray can be traced and processed without much trouble, but after one ray bounces off an object, it can turn into 10 rays, and those 10 can turn into 100, 1000…The increase is exponential, and the the calculation for all these rays will be time consuming.

Historically, computer hardware hasn’t been fast enough to use these techniques in real time, such as in video games. Moviemakers can take as long as they like to render a single frame, so they do it offline in render farms. Video games have only a fraction of a second. As a result, most real-time graphics rely on the another technique called rasterization.

PATH TRACING
Path tracing can be used to solve more complex lighting situations.
Path tracing is a type of ray tracing. When using path tracing for rendering, the rays only produce a single ray per bounce. The rays do not follow a defined line per bounce (to a light, for example), but rather shoot off in a random direction. The path tracing algorithm then takes a random sampling of all of the rays to create the final image. This results in sampling a variety of different types of lighting.

When a ray hits a surface it doesn’t trace a path to every light source, instead it bounces the ray off the surface and keeps bouncing it until it hits a light source or exhausts some bounce limit.
It then calculates the amount of light transferred all the way to the pixel, including any color information gathered from surfaces along the way.
It then averages out the values calculated from all the paths that were traced into the scene to get the final pixel color value.

It requires a ton of computing power and if you don’t send out enough rays per pixel or don’t trace the paths far enough into the scene then you end up with a very spotty image as many pixels fail to find any light sources from their rays. So when you increase the the samples per pixel, you can see the image quality becomes better and better.

Ray tracing tends to be more efficient than path tracing. Basically, the render time of a ray tracer depends on the number of polygons in the scene. The more polygons you have, the longer it will take.
Meanwhile, the rendering time of a path tracer can be indifferent to the number of polygons, but it is related to light situation: If you add a light, transparency, translucence, or other shader effects, the path tracer will slow down considerably.

Sources:
https://medium.com/@junyingw/future-of-gaming-rasterization-vs-ray-tracing-vs-path-tracing-32b334510f1f

https://www.reddit.com/r/explainlikeimfive/comments/8tim5q/eli5_whats_the_difference_among_rasterization_ray/

blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/

https://en.wikipedia.org/wiki/Rasterisation

https://www.dusterwald.com/2016/07/path-tracing-vs-ray-tracing/

https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing

Color Temperature of a light source describes the spectrum of light which is radiated from a theoretical “blackbody” (an ideal physical body that absorbs all radiation and incident light – neither reflecting it nor allowing it to pass through) with a given surface temperature.

https://en.wikipedia.org/wiki/Color_temperature

Or. Most simply it is a method of describing the color characteristics of light through a numerical value that corresponds to the color emitted by a light source, measured in degrees of Kelvin (K) on a scale from 1,000 to 10,000.

More accurately. The color temperature of a light source is the temperature of an ideal backbody that radiates light of comparable hue to that of the light source.

As such, the color temperature of a light source is a numerical measurement of its color appearance. It is based on the principle that any object will emit light if it is heated to a high enough temperature, and that the color of that light will shift in a predictable manner as the temperature is increased. The system is based on the color changes of a theoretical “blackbody radiator” as it is heated from a cold black to a white hot state.

So, why do we measure the hue of the light as a “temperature”? This was started in the late 1800s, when the British physicist William Kelvin heated a block of carbon. It glowed in the heat, producing a range of different colors at different temperatures. The black cube first produced a dim red light, increasing to a brighter yellow as the temperature went up, and eventually produced a bright blue-white glow at the highest temperatures. In his honor, Color Temperatures are measured in degrees Kelvin, which are a variation on Centigrade degrees. Instead of starting at the temperature water freezes, the Kelvin scale starts at “absolute zero,” which is -273 Centigrade.

More about black bodies here: https://www.pixelsham.com/2013/03/14/black-body-color

Details in the post

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Ethan Roffler
I recently had the honor of interviewing this VFX genius and gained great insight into what it takes to work in the entertainment industry. Keep in mind, these questions are coming from an artist’s perspective but can be applied to any creative individual looking for some wisdom from a professional. So grab a drink, sit back, and enjoy this fun and insightful conversation.

Ethan
To start, I just wanted to say thank you so much for taking the time for this interview!

Daniele
My pleasure.
When I started my career I struggled to find help. Even people in the industry at the time were not that helpful. Because of that, I decided very early on that I was going to do exactly the opposite. I spend most of my weekends talking or helping students. ;)

Ethan
That’s awesome! I have also come across the same struggle! Just a heads up, this will probably be the most informal interview you’ll ever have haha! Okay, so let’s start with a small introduction!

Daniele
Short introduction: I worked very hard and got lucky enough to work on great shows with great people. ;) Slightly longer version: I started working for a TV channel, very early, while I was learning about CG. Slowly made my way across the world, working along very great people and amazing shows. I learned that to be successful in this business, you have to really love what you do as much as respecting the people around you. What you do will improve to the final product; the way you work with people will make a difference in your life.

Ethan
How long have you been an artist?

Daniele
Loaded question. I believe I am still trying and craving to be one. After each production I finish I realize how much I still do not know. And how many things I would like to try. I guess in my CG Sup and generalist world, being an artist is about learning as much about the latest technologies and production cycles as I can, then putting that in practice. Having said that, I do consider myself a cinematographer first, as I have been doing that for about 25 years now.

Ethan
Words of true wisdom, the more I know the less I know:) How did you get your start in the industry?
How did you break into such a competitive field?

Daniele
There were not many schools when I started. It was all about a few magazines, some books, and pushing software around trying to learn how to make pretty images. Opportunities opened because of that knowledge! The true break was learning to work hard to achieve a Suspension of Disbelief in my work that people would recognize as such. It’s not something everyone can do, but I was fortunate to not be scared of working hard, being a quick learner and having very good supervisors and colleagues to learn from.

Ethan
Which do you think is better, having a solid art degree or a strong portfolio?

Daniele
Very good question. A strong portfolio will get you a job now. A solid strong degree will likely get you a job for a longer period. Let me digress here; Working as an artist is not about being an artist, it’s about making money as an artist. Most people fail to make that difference and have either a poor career or lack the understanding to make a stable one. One should never mix art with working as an artist. You can do both only if you understand business and are fair to yourself.

Ethan
That’s probably the most helpful answer to that question I have ever heard.
What’s some advice you can offer to someone just starting out who wants to break into the industry?

Daniele
Breaking in the industry is not just about knowing your art. It’s about knowing good business practices. Prepare a good demo reel based on the skill you are applying for; research all the places where you want to apply and why; send as many reels around; follow up each reel with a phone call. Business is all about right time, right place.

Ethan
A follow-up question to that is: Would you consider it a bad practice to send your demo reels out in mass quantity rather than focusing on a handful of companies to research and apply for?

Daniele
Depends how desperate you are… I would say research is a must. To improve your options, you need to know which company is working on what and what skills they are after. If you were selling vacuum cleaners you probably would not want to waste energy contacting shoemakers or cattle farmers.

Ethan
What do you think the biggest killer of creativity and productivity is for you?

Daniele
Money…If you were thinking as an artist. ;) If you were thinking about making money as an artist… then I would say “thinking that you work alone”.

Ethan
Best. Answer. Ever.
What are ways you fight complacency and maintain fresh ideas, outlooks, and perspectives

Daniele
Two things: Challenge yourself to go outside your comfort zone. And think outside of the box.

Ethan
What are the ways/habits you have that challenge yourself to get out of your comfort zone and think outside the box?

Daniele
If you think you are a good character painter, pick up a camera and go take pictures of amazing landscapes. If you think you are good only at painting or sketching, learn how to code in python. If you cannot solve a problem, that being a project or a person, learn to ask for help or learn about looking at the problem from various perspectives. If you are introvert, learn to be extrovert. And vice versa. And so on…

Ethan
How do you avoid burnout?

Daniele
Oh… I wish I learned about this earlier. I think anyone that has a passion in something is at risk of burning out. Artists, more than many, because we see the world differently and our passion goes deep. You avoid burnouts by thinking that you are in a long term plan and that you have an obligation to pay or repay your talent by supporting and cherishing yourself and your family, not your paycheck. You do this by treating your art as a business and using business skills when dealing with your career and using artistic skills only when you are dealing with a project itself.

Ethan
Looking back, what was a big defining moment for you?

Daniele
Recognizing that people around you, those being colleagues, friends or family, come first.
It changed my career overnight.

Ethan
Who are some of your personal heroes?

Daniele
Too many to list. Most recently… James Cameron; Joe Letteri; Lawrence Krauss; Richard Dawkins. Because they all mix science, art, and poetry in their own way.

Ethan
Last question:
What’s your dream job? ;)

Daniele
Teaching artists to be better at being business people… as it will help us all improve our lives and the careers we took…

Being a VFX artist is fundamentally based on mistrust.
This because schedules, pipelines, technology, creative calls… all have a native and naive instability to them that causes everyone to grow a genuine but beneficial lack of trust in the status quo. This is a fine balance act to build into your character. 

The VFX motto: “Love everyone but trust no one” is born on that.

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.

Wide color gamut, or WCG, is often lumped in with HDR. While they’re often found together, they’re not intrinsically linked. Where HDR is an increase in the dynamic range of the picture (with contrast and brighter highlights in particular), a TV’s wide color gamut coverage refers to how much of the new, larger color gamuts a TV can display.

Wide color gamuts only really matter for HDR video sources like UHD Blu-rays and some streaming video, as only HDR sources are meant to take advantage of the ability to display more colors.

www.cnet.com/how-to/what-is-wide-color-gamut-wcg/

Color depth is only one aspect of color representation, expressing the precision with which the amount of each primary can be expressed through a pixel; the other aspect is how broad a range of colors can be expressed (the gamut)

Image rendering bit depth

Wide color gamuts include a greater number of colors than what most current TVs can display, so the greater a TV’s coverage of a wide color gamut, the more colors a TV will be able to reproduce.

When we talk about a color space or color gamut we refer to the range of color values stored in an image. The perception of these color also requires a display that has been tuned with to resolve these color profiles at best. This is often referred to as a ‘viewer lut’.

So this comes also usually paired with an increase in bit depth, going from the old 8 bit system (256 shades per color, with the potential of over 16.7 million colors: 256 green x 256 blue x 256 red) to 10  (1024+ shades per color, with access to over a billion colors) or higher bits, like 12 bit (4096 shades per RGB for 68 billion colors).

The advantage of higher bit depth is in the ability to bias color with the minimum loss.

https://photo.stackexchange.com/questions/72116/whats-the-point-of-capturing-14-bit-images-and-editing-on-8-bit-monitors

For an extreme example, raising the brightness from a completely dark image allows for better reproduction, independently on the reproduction medium, due to the amount of data available at editing time:

https://www.cambridgeincolour.com/tutorials/dynamic-range.htm

https://www.hdrsoft.com/resources/dri.html#bit-depth

Note that the number of bits itself may be a misleading indication of the real dynamic range that the image reproduces — converting a Low Dynamic Range image to a higher bit depth does not change its dynamic range, of course.

• 8-bit images (i.e. 24 bits per pixel for a color image) are considered Low Dynamic Range.
• 16-bit images (i.e. 48 bits per pixel for a color image) resulting from RAW conversion are still considered Low Dynamic Range, even though the range of values they can encode is significantly higher than for 8-bit images (65536 versus 256). Note that converting a RAW file involves applying a tonal curve that compresses the dynamic range of the RAW data so that the converted image shows correctly on low dynamic range monitors. The need to adapt the output image file to the dynamic range of the display is the factor that dictates how much the dynamic range is compressed, not the output bit-depth. By using 16 instead of 8 bits, you will gain precision but you will not gain dynamic range.
• 32-bit images (i.e. 96 bits per pixel for a color image) are considered High Dynamic Range.Unlike 8- and 16-bit images which can take a finite number of values, 32-bit images are coded using floating point numbers, which means the values they can take is unlimited.It is important to note, though, that storing an image in a 32-bit HDR format is a necessary condition for an HDR image but not a sufficient one. When an image comes from a single capture with a standard camera, it will remain a Low Dynamic Range image,

Also note that bit depth and dynamic range are often confused as one, but are indeed separate concepts and there is no direct one to one relationship between them. Bit depth is about capacity, dynamic range is about the actual ratio of data stored.
The bit depth of a capturing or displaying device gives you an indication of its dynamic range capacity. That is, the highest dynamic range that the device would be capable of reproducing if all other constraints are eliminated.

https://rawpedia.rawtherapee.com/Bit_Depth

Finally, note that there are two ways to “count” bits for an image — either the number of bits per color channel (BPC) or the number of bits per pixel (BPP). A bit (0,1) is the smallest unit of data stored in a computer.

For a grayscale image, 8-bit means that each pixel can be one of 256 levels of gray (256 is 2 to the power 8).

For an RGB color image, 8-bit means that each one of the three color channels can be one of 256 levels of color.
Since each pixel is represented by 3 colors in this case, 8-bit per color channel actually means 24-bit per pixel.

Similarly, 16-bit for an RGB image means 65,536 levels per color channel and 48-bit per pixel.

To complicate matters, when an image is classified as 16-bit, it just means that it can store a maximum 65,535 values. It does not necessarily mean that it actually spans that range. If the camera sensors can not capture more than 12 bits of tonal values, the actual bit depth of the image will be at best 12-bit and probably less because of noise.

The following table attempts to summarize the above for the case of an RGB color image.

32-bit floats are often called “single-precision” floats, and 64-bit floats are often called “double-precision” floats. 16-bit floats therefore are called “half-precision” floats, or just “half floats”.

https://petapixel.com/2018/09/19/8-12-14-vs-16-bit-depth-what-do-you-really-need/

On a separate note, even Photoshop does not handle 16bit per channel. Photoshop does actually use 16-bits per channel. However, it treats the 16th digit differently – it is simply added to the value created from the first 15-digits. This is sometimes called 15+1 bits. This means that instead of 216 possible values (which would be 65,536 possible values) there are only 215+1 possible values (which is 32,768 +1 = 32,769 possible values).

Rec-601 (for the older SDTV format, very similar to rec-709) and Rec-709 (the HDTV’s recommended set of color standards, at times also referred to sRGB, although not exactly the same) are currently the most spread color formats and hardware configurations in the world.

Following those you can find the larger P3 gamut, more commonly used in theaters and in digital production houses (with small variations and improvements to color coverage), as well as most of best 4K/WCG TVs.

And a new standard is now promoted against P3, referred to Rec-2020 and UHDTV.

It is still debatable if this is going to be adopted at consumer level beyond the P3, mainly due to lack of hardware supporting it. But initial tests do prove that it would be a future proof investment.

www.colour-science.org/anders-langlands/

Rec. 2020 is ultimately designed for television, and not cinema. Therefore, it is to be expected that its properties must behave according to current signal processing standards. In this respect, its foundation is based on current HD and SD video signal characteristics.

As far as color bit depth is concerned, it allows for a maximum of 12 bits, which is more than enough for humans.

Comparing standards, REC-709 covers 35.9% of the human visible spectrum. P3 45.5%. And REC-2020 75.8%.
https://www.avsforum.com/forum/166-lcd-flat-panel-displays/2812161-what-color-volume.html

Comparing coverage to hardware devices

To note that all the new standards generally score very high on the Pointer’s Gamut chart. But with REC-2020 scoring 99.9% vs P3 at 88.2%.
www.tftcentral.co.uk/articles/pointers_gamut.htm

https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications

The Pointer’s gamut is (an approximation of) the gamut of real surface colors as can be seen by the human eye, based on the research by Michael R. Pointer (1980). What this means is that every color that can be reflected by the surface of an object of any material is inside the Pointer’s gamut. Basically establishing a widely respected target for color reproduction. Visually, Pointers Gamut represents the colors we see about us in the natural world. Colors outside Pointers Gamut include those that do not occur naturally, such as neon lights and computer-generated colors possible in animation. Which would partially be accounted for with the new gamuts.

cinepedia.com/picture/color-gamut/

Not all current TVs can support the full spread of the new gamuts. Here is a list of modern TVs’ color coverage in percentage:
www.rtings.com/tv/tests/picture-quality/wide-color-gamut-rec-709-dci-p3-rec-2020

There are no TVs that can come close to displaying all the colors within Rec.2020, and there likely won’t be for at least a few years. However, to help future-proof the technology, Rec.2020 support is already baked into the HDR spec. That means that the same genuine HDR media that fills the DCI P3 space on a compatible TV now, will in a few years also fill Rec.2020 on a TV supporting that larger space.

Rec.2020’s main gains are in the number of new tones of green that it will display, though it also offers improvements to the number of blue and red colors as well. Altogether, Rec.2020 will cover about 75% of the visual spectrum, which is a sizeable increase in coverage even over DCI P3.

Dolby Vision

https://www.highdefdigest.com/news/show/what-is-dolby-vision/39049

https://www.techhive.com/article/3237232/dolby-vision-vs-hdr10-which-is-best.html

Dolby Vision is a proprietary end-to-end High Dynamic Range (HDR) format that covers content creation and playback through select cinemas, Ultra HD displays, and 4K titles. Like other HDR standards, the process uses expanded brightness to improve contrast between dark and light aspects of an image, bringing out deeper black levels and more realistic details in specular highlights — like the sun reflecting off of an ocean — in specially graded Dolby Vision material.

The iPhone 12 Pro gets the ability to record 4K 10-bit HDR video. According to Apple, it is the very first smartphone that is capable of capturing Dolby Vision HDR.

The iPhone 12 Pro takes two separate exposures and runs them through Apple’s custom image signal processor to create a histogram, which is a graph of the tonal values in each frame. The Dolby Vision metadata is then generated based on that histogram. In Laymen’s terms, it is essentially doing real-time grading while you are shooting. This is only possible due to the A14 Bionic chip.

Dolby Vision also allows for 12-bit color, as opposed to HDR10’s and HDR10+’s 10-bit color. While no retail TV we’re aware of supports 12-bit color, Dolby claims it can be down-sampled in such a way as to render 10-bit color more accurately.

Resources for more reading:

https://www.avsforum.com/forum/166-lcd-flat-panel-displays/2812161-what-color-volume.html

wolfcrow.com/say-hello-to-rec-2020-the-color-space-of-the-future/

www.cnet.com/news/ultra-hd-tv-color-part-ii-the-future/