COMPOSITION
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Composition – These are the basic lighting techniques you need to know for photography and film
Read more: Composition – These are the basic lighting techniques you need to know for photography and filmhttp://www.diyphotography.net/basic-lighting-techniques-need-know-photography-film/
Amongst the basic techniques, there’s…
1- Side lighting – Literally how it sounds, lighting a subject from the side when they’re faced toward you
2- Rembrandt lighting – Here the light is at around 45 degrees over from the front of the subject, raised and pointing down at 45 degrees
3- Back lighting – Again, how it sounds, lighting a subject from behind. This can help to add drama with silouettes
4- Rim lighting – This produces a light glowing outline around your subject
5- Key light – The main light source, and it’s not necessarily always the brightest light source
6- Fill light – This is used to fill in the shadows and provide detail that would otherwise be blackness
7- Cross lighting – Using two lights placed opposite from each other to light two subjects
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HuggingFace ai-comic-factory – a FREE AI Comic Book Creator
Read more: HuggingFace ai-comic-factory – a FREE AI Comic Book Creatorhttps://huggingface.co/spaces/jbilcke-hf/ai-comic-factory
this is the epic story of a group of talented digital artists trying to overcame daily technical challenges to achieve incredibly photorealistic projects of monsters and aliens
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Composition – 5 tips for creating perfect cinematic lighting and making your work look stunning
Read more: Composition – 5 tips for creating perfect cinematic lighting and making your work look stunninghttp://www.diyphotography.net/5-tips-creating-perfect-cinematic-lighting-making-work-look-stunning/
1. Learn the rules of lighting
2. Learn when to break the rules
3. Make your key light larger
4. Reverse keying
5. Always be backlighting
DESIGN
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Glenn Marshall – The Crow
Read more: Glenn Marshall – The CrowCreated with AI ‘Style Transfer’ processes to transform video footage into AI video art.
COLOR
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Willem Zwarthoed – Aces gamut in VFX production pdf
https://www.provideocoalition.com/color-management-part-12-introducing-aces/
Local copy:
https://www.slideshare.net/hpduiker/acescg-a-common-color-encoding-for-visual-effects-applications
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Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental process
Read more: Christopher Butler – Understanding the Eye-Mind Connection – Vision is a mental processhttps://www.chrbutler.com/understanding-the-eye-mind-connection
The intricate relationship between the eyes and the brain, often termed the eye-mind connection, reveals that vision is predominantly a cognitive process. This understanding has profound implications for fields such as design, where capturing and maintaining attention is paramount. This essay delves into the nuances of visual perception, the brain’s role in interpreting visual data, and how this knowledge can be applied to effective design strategies.
This cognitive aspect of vision is evident in phenomena such as optical illusions, where the brain interprets visual information in a way that contradicts physical reality. These illusions underscore that what we “see” is not merely a direct recording of the external world but a constructed experience shaped by cognitive processes.
Understanding the cognitive nature of vision is crucial for effective design. Designers must consider how the brain processes visual information to create compelling and engaging visuals. This involves several key principles:
- Attention and Engagement
- Visual Hierarchy
- Cognitive Load Management
- Context and Meaning
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A Brief History of Color in Art
Read more: A Brief History of Color in Artwww.artsy.net/article/the-art-genome-project-a-brief-history-of-color-in-art
Of all the pigments that have been banned over the centuries, the color most missed by painters is likely Lead White.
This hue could capture and reflect a gleam of light like no other, though its production was anything but glamorous. The 17th-century Dutch method for manufacturing the pigment involved layering cow and horse manure over lead and vinegar. After three months in a sealed room, these materials would combine to create flakes of pure white. While scientists in the late 19th century identified lead as poisonous, it wasn’t until 1978 that the United States banned the production of lead white paint.
More reading:
www.canva.com/learn/color-meanings/https://www.infogrades.com/history-events-infographics/bizarre-history-of-colors/
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What Is The Resolution and view coverage Of The human Eye. And what distance is TV at best?
Read more: What Is The Resolution and view coverage Of The human Eye. And what distance is TV at best?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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Photography basics: Color Temperature and White Balance
Read more: Photography basics: Color Temperature and White BalanceColor 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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Space bodies’ components and light spectroscopy
www.plutorules.com/page-111-space-rocks.html
This help’s us understand the composition of components in/on solar system bodies.
Dips in the observed light spectrum, also known as, lines of absorption occur as gasses absorb energy from light at specific points along the light spectrum.
These dips or darkened zones (lines of absorption) leave a finger print which identify elements and compounds.
In this image the dark absorption bands appear as lines of emission which occur as the result of emitted not reflected (absorbed) light.
Lines of absorption
Lines of emission
LIGHTING
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
Read more: DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ballhttps://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
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HDRI shooting and editing by Xuan Prada and Greg Zaal
www.xuanprada.com/blog/2014/11/3/hdri-shooting
http://blog.gregzaal.com/2016/03/16/make-your-own-hdri/
http://blog.hdrihaven.com/how-to-create-high-quality-hdri/
Shooting checklist
- Full coverage of the scene (fish-eye shots)
- Backplates for look-development (including ground or floor)
- Macbeth chart for white balance
- Grey ball for lighting calibration
- Chrome ball for lighting orientation
- Basic scene measurements
- Material samples
- Individual HDR artificial lighting sources if required
Methodology
- Plant the tripod where the action happens, stabilise it and level it
- Set manual focus
- Set white balance
- Set ISO
- Set raw+jpg
- Set apperture
- Metering exposure
- Set neutral exposure
- Read histogram and adjust neutral exposure if necessary
- Shot slate (operator name, location, date, time, project code name, etc)
- Set auto bracketing
- Shot 5 to 7 exposures with 3 stops difference covering the whole environment
- Place the aromatic kit where the tripod was placed, and take 3 exposures. Keep half of the grey sphere hit by the sun and half in shade.
- Place the Macbeth chart 1m away from tripod on the floor and take 3 exposures
- Take backplates and ground/floor texture references
- Shoot reference materials
- Write down measurements of the scene, specially if you are shooting interiors.
- If shooting artificial lights take HDR samples of each individual lighting source.
Exposures starting point
- Day light sun visible ISO 100 F22
- Day light sun hidden ISO 100 F16
- Cloudy ISO 320 F16
- Sunrise/Sunset ISO 100 F11
- Interior well lit ISO 320 F16
- Interior ambient bright ISO 320 F10
- Interior bad light ISO 640 F10
- Interior ambient dark ISO 640 F8
- Low light situation ISO 640 F5
NOTE: The goal is to clean the initial individual brackets before or at merging time as much as possible.
This means:- keeping original shooting metadata
- de-fringing
- removing aberration (through camera lens data or automatically)
- at 32 bit
- in ACEScg (or ACES) wherever possible
Here are the tips for using the chromatic ball in VFX projects, written in English:
https://www.linkedin.com/posts/bellrodrigo_here-are-the-tips-for-using-the-chromatic-activity-7200950595438940160-AGBpTips for Using the Chromatic Ball in VFX Projects**
The chromatic ball is an invaluable tool in VFX work, helping to capture lighting and reflection data crucial for integrating CGI elements seamlessly. Here are some tips to maximize its effectiveness:
1. **Positioning**:
– Place the chromatic ball in the same lighting conditions as the main subject. Ensure it is visible in the camera frame but not obstructing the main action.
– Ideally, place the ball where the CGI elements will be integrated to match the lighting and reflections accurately.2. **Recording Reference Footage**:
– Capture reference footage of the chromatic ball at the beginning and end of each scene or lighting setup. This ensures you have consistent lighting data for the entire shoot.3. **Consistent Angles**:
– Use consistent camera angles and heights when recording the chromatic ball. This helps in comparing and matching lighting setups across different shots.4. **Combine with a Gray Ball**:
– Use a gray ball alongside the chromatic ball. The gray ball provides a neutral reference for exposure and color balance, complementing the chromatic ball’s reflection data.5. **Marking Positions**:
– Mark the position of the chromatic ball on the set to ensure consistency when shooting multiple takes or different camera angles.6. **Lighting Analysis**:
– Analyze the chromatic ball footage to understand the light sources, intensity, direction, and color temperature. This information is crucial for creating realistic CGI lighting and shadows.7. **Reflection Analysis**:
– Use the chromatic ball to capture the environment’s reflections. This helps in accurately reflecting the CGI elements within the same scene, making them blend seamlessly.8. **Use HDRI**:
– Capture High Dynamic Range Imagery (HDRI) of the chromatic ball. HDRI provides detailed lighting information and can be used to light CGI scenes with greater realism.9. **Communication with VFX Team**:
– Ensure that the VFX team is aware of the chromatic ball’s data and how it was captured. Clear communication ensures that the data is used effectively in post-production.10. **Post-Production Adjustments**:
– In post-production, use the chromatic ball data to adjust the CGI elements’ lighting and reflections. This ensures that the final output is visually cohesive and realistic. -
domeble – Hi-Resolution CGI Backplates and 360° HDRI
When collecting hdri make sure the data supports basic metadata, such as:
- Iso
- Aperture
- Exposure time or shutter time
- Color temperature
- Color space Exposure value (what the sensor receives of the sun intensity in lux)
- 7+ brackets (with 5 or 6 being the perceived balanced exposure)
In image processing, computer graphics, and photography, high dynamic range imaging (HDRI or just HDR) is a set of techniques that allow a greater dynamic range of luminances (a Photometry measure of the luminous intensity per unit area of light travelling in a given direction. It describes the amount of light that passes through or is emitted from a particular area, and falls within a given solid angle) between the lightest and darkest areas of an image than standard digital imaging techniques or photographic methods. This wider dynamic range allows HDR images to represent more accurately the wide range of intensity levels found in real scenes ranging from direct sunlight to faint starlight and to the deepest shadows.
The two main sources of HDR imagery are computer renderings and merging of multiple photographs, which in turn are known as low dynamic range (LDR) or standard dynamic range (SDR) images. Tone Mapping (Look-up) techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Photography
In photography, dynamic range is measured in Exposure Values (in photography, exposure value denotes all combinations of camera shutter speed and relative aperture that give the same exposure. The concept was developed in Germany in the 1950s) differences or stops, between the brightest and darkest parts of the image that show detail. An increase of one EV or one stop is a doubling of the amount of light.
The human response to brightness is well approximated by a Steven’s power law, which over a reasonable range is close to logarithmic, as described by the Weber�Fechner law, which is one reason that logarithmic measures of light intensity are often used as well.
HDR is short for High Dynamic Range. It’s a term used to describe an image which contains a greater exposure range than the “black” to “white” that 8 or 16-bit integer formats (JPEG, TIFF, PNG) can describe. Whereas these Low Dynamic Range images (LDR) can hold perhaps 8 to 10 f-stops of image information, HDR images can describe beyond 30 stops and stored in 32 bit images.
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Rec-2020 – TVs new color gamut standard used by Dolby Vision?
https://www.hdrsoft.com/resources/dri.html#bit-depth
The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.
For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.
For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.
For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.
The Dynamic Range of real-world scenes can be quite high — ratios of 100,000:1 are common in the natural world. An HDR (High Dynamic Range) image stores pixel values that span the whole tonal range of real-world scenes. Therefore, an HDR image is encoded in a format that allows the largest range of values, e.g. floating-point values stored with 32 bits per color channel. Another characteristics of an HDR image is that it stores linear values. This means that the value of a pixel from an HDR image is proportional to the amount of light measured by the camera.
For TVs HDR is great, but it’s not the only new TV feature worth discussing.
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.
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:
For reference, 8-bit images (i.e. 24 bits per pixel for a color image) are considered Low Dynamic Range.
They can store around 5 stops of light and each pixel carry a value from 0 (black) to 255 (white).
As a comparison, DSLR cameras can capture ~12-15 stops of light and they use RAW files to store the information.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.
Type of digital support Bit depth per color channel Bit depth per pixel FStops Theoretical maximum Dynamic Range Reality 8-bit 8 24 8 256:1 most consumer images 12-bit CCD 12 36 12 4,096:1 real maximum limited by noise 14-bit CCD 14 42 14 16,384:1 real maximum limited by noise 16-bit TIFF (integer) 16 48 16 65,536:1 bit-depth in this case is not directly related to the dynamic range captured 16-bit float EXR 16 48 30 65,536:1 values are distributed more closely in the (lower) darker tones than in the (higher) lighter ones, thus allowing for a more accurate description of the tones more significant to humans. The range of normalized 16-bit floats can represent thirty stops of information with 1024 steps per stop. We have eighteen and a half stops over middle gray, and eleven and a half below. The denormalized numbers provide an additional ten stops with decreasing precision per stop.
http://download.nvidia.com/developer/GPU_Gems/CD_Image/Image_Processing/OpenEXR/OpenEXR-1.0.6/doc/#recsHDR image (e.g. Radiance format) 32 96 “infinite” 4.3 billion:1 real maximum limited by the captured dynamic range 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.htmlComparing 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.htmhttps://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-2020There 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/
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What’s the Difference Between Ray Casting, Ray Tracing, Path Tracing and Rasterization? Physical light tracing…
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.blogs.nvidia.com/blog/2018/03/19/whats-difference-between-ray-tracing-rasterization/
https://en.wikipedia.org/wiki/Rasterisation
https://www.quora.com/Whats-the-difference-between-ray-tracing-and-path-tracing
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