COMPOSITION
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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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Photography basics: Camera Aspect Ratio, Sensor Size and Depth of Field – resolutions
Read more: Photography basics: Camera Aspect Ratio, Sensor Size and Depth of Field – resolutionshttp://www.shutterangle.com/2012/cinematic-look-aspect-ratio-sensor-size-depth-of-field/
http://www.shutterangle.com/2012/film-video-aspect-ratio-artistic-choice/
DESIGN
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James Gerde – The way the leaves dance in the rain
Read more: James Gerde – The way the leaves dance in the rainhttps://www.instagram.com/gerdegotit/reel/C6s-2r2RgSu/
Since spending a lot of time recently with SDXL I’ve since made my way back to SD 1.5
While the models overall have less fidelity. There is just no comparing to the current motion models we have available for animatediff with 1.5 models.
To date this is one of my favorite pieces. Not because I think it’s even the best it can be. But because the workflow adjustments unlocked some very important ideas I can’t wait to try out.
Performance by @silkenkelly and @itxtheballerina on IG
COLOR
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What light is best to illuminate gems for resale
Read more: What light is best to illuminate gems for resalewww.palagems.com/gem-lighting2
Artificial light sources, not unlike the diverse phases of natural light, vary considerably in their properties. As a result, some lamps render an object’s color better than others do.
The most important criterion for assessing the color-rendering ability of any lamp is its spectral power distribution curve.
Natural daylight varies too much in strength and spectral composition to be taken seriously as a lighting standard for grading and dealing colored stones. For anything to be a standard, it must be constant in its properties, which natural light is not.
For dealers in particular to make the transition from natural light to an artificial light source, that source must offer:
1- A degree of illuminance at least as strong as the common phases of natural daylight.
2- Spectral properties identical or comparable to a phase of natural daylight.A source combining these two things makes gems appear much the same as when viewed under a given phase of natural light. From the viewpoint of many dealers, this corresponds to a naturalappearance.
The 6000° Kelvin xenon short-arc lamp appears closest to meeting the criteria for a standard light source. Besides the strong illuminance this lamp affords, its spectrum is very similar to CIE standard illuminants of similar color temperature.
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HDR and Color
Read more: HDR and Colorhttps://www.soundandvision.com/content/nits-and-bits-hdr-and-color
In HD we often refer to the range of available colors as a color gamut. Such a color gamut is typically plotted on a two-dimensional diagram, called a CIE chart, as shown in at the top of this blog. Each color is characterized by its x/y coordinates.
Good enough for government work, perhaps. But for HDR, with its higher luminance levels and wider color, the gamut becomes three-dimensional.
For HDR the color gamut therefore becomes a characteristic we now call the color volume. It isn’t easy to show color volume on a two-dimensional medium like the printed page or a computer screen, but one method is shown below. As the luminance becomes higher, the picture eventually turns to white. As it becomes darker, it fades to black. The traditional color gamut shown on the CIE chart is simply a slice through this color volume at a selected luminance level, such as 50%.
Three different color volumes—we still refer to them as color gamuts though their third dimension is important—are currently the most significant. The first is BT.709 (sometimes referred to as Rec.709), the color gamut used for pre-UHD/HDR formats, including standard HD.
The largest is known as BT.2020; it encompasses (roughly) the range of colors visible to the human eye (though ET might find it insufficient!).
Between these two is the color gamut used in digital cinema, known as DCI-P3.
sRGB
D65
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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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About green screens
Read more: About green screenshackaday.com/2015/02/07/how-green-screen-worked-before-computers/
www.newtek.com/blog/tips/best-green-screen-materials/
www.chromawall.com/blog//chroma-key-green
Chroma Key Green, the color of green screens is also known as Chroma Green and is valued at approximately 354C in the Pantone color matching system (PMS).
Chroma Green can be broken down in many different ways. Here is green screen green as other values useful for both physical and digital production:
Green Screen as RGB Color Value: 0, 177, 64
Green Screen as CMYK Color Value: 81, 0, 92, 0
Green Screen as Hex Color Value: #00b140
Green Screen as Websafe Color Value: #009933Chroma Key Green is reasonably close to an 18% gray reflectance.
Illuminate your green screen with an uniform source with less than 2/3 EV variation.
The level of brightness at any given f-stop should be equivalent to a 90% white card under the same lighting. -
OLED vs QLED – What TV is better?
Read more: OLED vs QLED – What TV is better?
Supported by LG, Philips, Panasonic and Sony sell the OLED system TVs.
OLED stands for “organic light emitting diode.”
It is a fundamentally different technology from LCD, the major type of TV today.
OLED is “emissive,” meaning the pixels emit their own light.Samsung is branding its best TVs with a new acronym: “QLED”
QLED (according to Samsung) stands for “quantum dot LED TV.”
It is a variation of the common LED LCD, adding a quantum dot film to the LCD “sandwich.”
QLED, like LCD, is, in its current form, “transmissive” and relies on an LED backlight.OLED is the only technology capable of absolute blacks and extremely bright whites on a per-pixel basis. LCD definitely can’t do that, and even the vaunted, beloved, dearly departed plasma couldn’t do absolute blacks.
QLED, as an improvement over OLED, significantly improves the picture quality. QLED can produce an even wider range of colors than OLED, which says something about this new tech. QLED is also known to produce up to 40% higher luminance efficiency than OLED technology. Further, many tests conclude that QLED is far more efficient in terms of power consumption than its predecessor, OLED.
When analyzing TVs color, it may be beneficial to consider at least 3 elements:
“Color Depth”, “Color Gamut”, and “Dynamic Range”.Color Depth (or “Bit-Depth”, e.g. 8-bit, 10-bit, 12-bit) determines how many distinct color variations (tones/shades) can be viewed on a given display.
Color Gamut (e.g. WCG) determines which specific colors can be displayed from a given “Color Space” (Rec.709, Rec.2020, DCI-P3) (i.e. the color range).
Dynamic Range (SDR, HDR) determines the luminosity range of a specific color – from its darkest shade (or tone) to its brightest.
The overall brightness range of a color will be determined by a display’s “contrast ratio”, that is, the ratio of luminance between the darkest black that can be produced and the brightest white.
Color Volume is the “Color Gamut” + the “Dynamic/Luminosity Range”.
A TV’s Color Volume will not only determine which specific colors can be displayed (the color range) but also that color’s luminosity range, which will have an affect on its “brightness”, and “colorfulness” (intensity and saturation).The better the colour volume in a TV, the closer to life the colours appear.
QLED TV can express nearly all of the colours in the DCI-P3 colour space, and of those colours, express 100% of the colour volume, thereby producing an incredible range of colours.
With OLED TV, when the image is too bright, the percentage of the colours in the colour volume produced by the TV drops significantly. The colours get washed out and can only express around 70% colour volume, making the picture quality drop too.
Note. OLED TV uses organic material, so it may lose colour expression as it ages.
Resources for more reading and comparison below
www.avsforum.com/forum/166-lcd-flat-panel-displays/2812161-what-color-volume.html
www.newtechnologytv.com/qled-vs-oled/
news.samsung.com/za/qled-tv-vs-oled-tv
www.cnet.com/news/qled-vs-oled-samsungs-tv-tech-and-lgs-tv-tech-are-not-the-same/
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Björn Ottosson – OKlch color space
Read more: Björn Ottosson – OKlch color spaceBjörn Ottosson proposed OKlch in 2020 to create a color space that can closely mimic how color is perceived by the human eye, predicting perceived lightness, chroma, and hue.
The OK in OKLCH stands for Optimal Color.
- L: Lightness (the perceived brightness of the color)
- C: Chroma (the intensity or saturation of the color)
- H: Hue (the actual color, such as red, blue, green, etc.)
Also read:
LIGHTING
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domeble – Hi-Resolution CGI Backplates and 360° HDRI
Read more: domeble – Hi-Resolution CGI Backplates and 360° HDRIWhen 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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Disney’s Moana Island Scene – Free data set
Read more: Disney’s Moana Island Scene – Free data sethttps://www.disneyanimation.com/resources/moana-island-scene/
This data set contains everything necessary to render a version of the Motunui island featured in the 2016 film Moana.
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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.”
![[gamma correction test]](http://www.madore.org/~david/misc/color/gammatest.png "sRGB gamma correction test")
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