COLOR

  • Photography Basics : Spectral Sensitivity Estimation Without a Camera

    https://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/

     

    A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.

     

    To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.

     

    Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.

     

     

     

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

    https://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:

    1. Attention and Engagement
    2. Visual Hierarchy
    3. Cognitive Load Management
    4. Context and Meaning

     

     

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  • Björn Ottosson – How software gets color wrong

    https://bottosson.github.io/posts/colorwrong/

     

    Most software around us today are decent at accurately displaying colors. Processing of colors is another story unfortunately, and is often done badly.

     

    To understand what the problem is, let’s start with an example of three ways of blending green and magenta:

    • Perceptual blend – A smooth transition using a model designed to mimic human perception of color. The blending is done so that the perceived brightness and color varies smoothly and evenly.
    • Linear blend – A model for blending color based on how light behaves physically. This type of blending can occur in many ways naturally, for example when colors are blended together by focus blur in a camera or when viewing a pattern of two colors at a distance.
    • sRGB blend – This is how colors would normally be blended in computer software, using sRGB to represent the colors. 

     

    Let’s look at some more examples of blending of colors, to see how these problems surface more practically. The examples use strong colors since then the differences are more pronounced. This is using the same three ways of blending colors as the first example.

     

    Instead of making it as easy as possible to work with color, most software make it unnecessarily hard, by doing image processing with representations not designed for it. Approximating the physical behavior of light with linear RGB models is one easy thing to do, but more work is needed to create image representations tailored for image processing and human perception.

     

    Also see:

    https://www.pixelsham.com/2022/04/05/bjorn-ottosson-okhsv-and-okhsl-two-new-color-spaces-for-color-picking/

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  • Sensitivity of human eye

    http://www.wikilectures.eu/index.php/Spectral_sensitivity_of_the_human_eye

    http://www.normankoren.com/Human_spectral_sensitivity_small.jpg

    Spectral sensitivity of eye is influenced by light intensity. And the light intensity determines the level of activity of cones cell and rod cell. This is the main characteristic of human vision. Sensitivity to individual colors, in other words, wavelengths of the light spectrum, is explained by the RGB (red-green-blue) theory. This theory assumed that there are three kinds of cones. It’s selectively sensitive to red (700-630 nm), green (560-500 nm), and blue (490-450 nm) light. And their mutual interaction allow to perceive all colors of the spectrum.

    http://weeklysciencequiz.blogspot.com/2013/01/violet-skies-are-for-birds.html

     

     

    Sensitivity of human eye Sensitivity of human eyes to light increase with the decrease in light intensity. In day-light condition, the cones cell is responding to this condition. And the eye is most sensitive at 555 nm. In darkness condition, the rod cell is responding to this condition. And the eye is most sensitive at 507 nm.

    As light intensity decreases, cone function changes more effective way. And when decrease the light intensity, it prompt to accumulation of rhodopsin. Furthermore, in activates rods, it allow to respond to stimuli of light in much lower intensity.

     

    https://www.nde-ed.org/EducationResources/CommunityCollege/PenetrantTest/Introduction/lightresponse.htm

    The three curves in the figure above shows the normalized response of an average human eye to various amounts of ambient light. The shift in sensitivity occurs because two types of photoreceptors called cones and rods are responsible for the eye’s response to light. The curve on the right shows the eye’s response under normal lighting conditions and this is called the photopic response. The cones respond to light under these conditions.

     

    As mentioned previously, cones are composed of three different photo pigments that enable color perception. This curve peaks at 555 nanometers, which means that under normal lighting conditions, the eye is most sensitive to a yellowish-green color. When the light levels drop to near total darkness, the response of the eye changes significantly as shown by the scotopic response curve on the left. At this level of light, the rods are most active and the human eye is more sensitive to the light present, and less sensitive to the range of color. Rods are highly sensitive to light but are comprised of a single photo pigment, which accounts for the loss in ability to discriminate color. At this very low light level, sensitivity to blue, violet, and ultraviolet is increased, but sensitivity to yellow and red is reduced. The heavier curve in the middle represents the eye’s response at the ambient light level found in a typical inspection booth. This curve peaks at 550 nanometers, which means the eye is most sensitive to yellowish-green color at this light level. Fluorescent penetrant inspection materials are designed to fluoresce at around 550 nanometers to produce optimal sensitivity under dim lighting conditions.

     

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  • Image rendering bit depth

    The terms 8-bit, 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.

     

     

    Here’s a simple explanation of each.

     

    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.

     

    16-bit: This format is commonly referred to as “half-precision.” It uses 16 bits of data to represent color values for each pixel. With 16 bits, you can have 65,536 discrete levels of color, allowing for relatively high precision and smooth gradients. However, it has a limited dynamic range, meaning it cannot accurately represent extremely bright or dark values. It is commonly used for regular images and textures.

     

    16-bit float: This format is an extension of the 16-bit format but uses floating-point numbers instead of fixed integers. Floating-point numbers allow for more precise calculations and a larger dynamic range. In this case, the 16 bits are used to store both the color value and the exponent, which controls the range of values that can be represented. The 16-bit float format provides better accuracy and a wider dynamic range than regular 16-bit, making it useful for high-dynamic-range imaging (HDRI) and computations that require more precision.

     

    32-bit: (i.e. 96 bits per pixel for a color image) are considered High Dynamic Range. This format, also known as “full-precision” or “float,” uses 32 bits to represent color values and offers the highest precision and dynamic range among the three options. With 32 bits, you have a significantly larger number of discrete levels, allowing for extremely accurate color representation, smooth gradients, and a wide range of brightness values. It is commonly used for professional rendering, visual effects, and scientific applications where maximum precision is required.

     

    Bits and HDR coverage

    High Dynamic Range (HDR) images are designed to capture a wide range of luminance values, from the darkest shadows to the brightest highlights, in order to reproduce a scene with more accuracy and detail. The bit depth of an image refers to the number of bits used to represent each pixel’s color information. When comparing 32-bit float and 16-bit float HDR images, the drop in accuracy primarily relates to the precision of the color information.

     

    A 32-bit float HDR image offers a higher level of precision compared to a 16-bit float HDR image. In a 32-bit float format, each color channel (red, green, and blue) is represented by 32 bits, allowing for a larger range of values to be stored. This increased precision enables the image to retain more details and subtleties in color and luminance.

     

    On the other hand, a 16-bit float HDR image utilizes 16 bits per color channel, resulting in a reduced range of values that can be represented. This lower precision leads to a loss of fine details and color nuances, especially in highly contrasted areas of the image where there are significant differences in luminance.

     

    The drop in accuracy between 32-bit and 16-bit float HDR images becomes more noticeable as the exposure range of the scene increases. Exposure range refers to the span between the darkest and brightest areas of an image. In scenes with a limited exposure range, where the luminance differences are relatively small, the loss of accuracy may not be as prominent or perceptible. These images usually are around 8-10 exposure levels.

     

    However, in scenes with a wide exposure range, such as a landscape with deep shadows and bright highlights, the reduced precision of a 16-bit float HDR image can result in visible artifacts like color banding, posterization, and loss of detail in both shadows and highlights. The image may exhibit abrupt transitions between tones or colors, which can appear unnatural and less realistic.

     

    To provide a rough estimate, it is often observed that exposure values beyond approximately ±6 to ±8 stops from the middle gray (18% reflectance) may be more prone to accuracy issues in a 16-bit float format. This range may vary depending on the specific implementation and encoding scheme used.

     

    To summarize, the drop in accuracy between 32-bit and 16-bit float HDR images is mainly related to the reduced precision of color information. This decrease in precision becomes more apparent in scenes with a wide exposure range, affecting the representation of fine details and leading to visible artifacts in the image.

     

    In practice, this means that exposure values beyond a certain range will experience a loss of accuracy and detail when stored in a 16-bit float format. The exact range at which this loss occurs depends on the encoding scheme and the specific implementation. However, in general, extremely bright or extremely dark values that fall outside the representable range may be subject to quantization errors, resulting in loss of detail, banding, or other artifacts.

     

    HDRs used for lighting purposes are usually slightly convolved to improve on sampling speed and removing specular artefacts. To that extent, 16 bit float HDRIs tend to me most used in CG cycles.

     

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  • No one could see the colour blue until modern times

    https://www.businessinsider.com/what-is-blue-and-how-do-we-see-color-2015-2

    The way that humans see the world… until we have a way to describe something, even something so fundamental as a colour, we may not even notice that something it’s there.

     

    Ancient languages didn’t have a word for blue — not Greek, not Chinese, not Japanese, not Hebrew, not Icelandic cultures. And without a word for the colour, there’s evidence that they may not have seen it at all.

    https://www.wnycstudios.org/story/211119-colors

     

    Every language first had a word for black and for white, or dark and light. The next word for a colour to come into existence — in every language studied around the world — was red, the colour of blood and wine.

    After red, historically, yellow appears, and later, green (though in a couple of languages, yellow and green switch places). The last of these colours to appear in every language is blue.

     

    The only ancient culture to develop a word for blue was the Egyptians — and as it happens, they were also the only culture that had a way to produce a blue dye.

    https://mymodernmet.com/shades-of-blue-color-history/



    https://www.msn.com/en-us/news/technology/scientists-recreate-lost-recipes-for-a-5-000-year-old-egyptian-blue-dye/ar-AA1FXcj1

    Assessment of process variability and color in synthesized and ancient Egyptian blue pigments | npj Heritage Science

    The approximately 5,000-year-old dye wasn’t a single color, but instead encompassed a range of hues, from deep blues to duller grays and greens. Artisans first crafted Egyptian blue during the Fourth Dynasty (roughly 2613 to 2494 BCE) from recipes reliant on calcium-copper silicate. These techniques were later adopted by Romans in lieu of more expensive materials like lapis lazuli and turquoise. But the additional ingredient lists were lost to history by the time of the Renaissance. 

    McCloy’s team confirmed that cuprorivaite—the naturally occurring mineral equivalent to Egyptian blue—remains the primary color influence in each hue. Despite the presence of other components, Egyptian blue appears as a uniform color after the cuprorivaite becomes encased in colorless particles such as silicate during the heating process.

     

    Considered to be the first ever synthetically produced color pigment, Egyptian blue (also known as cuprorivaite) was created around 2,200 B.C. It was made from ground limestone mixed with sand and a copper-containing mineral, such as azurite or malachite, which was then heated between 1470 and 1650°F. The result was an opaque blue glass which then had to be crushed and combined with thickening agents such as egg whites to create a long-lasting paint or glaze.

     

     

    If you think about it, blue doesn’t appear much in nature — there aren’t animals with blue pigments (except for one butterfly, Obrina Olivewing, all animals generate blue through light scattering), blue eyes are rare (also blue through light scattering), and blue flowers are mostly human creations. There is, of course, the sky, but is that really blue?

     

     

    So before we had a word for it, did people not naturally see blue? Do you really see something if you don’t have a word for it?

     

    A researcher named Jules Davidoff traveled to Namibia to investigate this, where he conducted an experiment with the Himba tribe, who speak a language that has no word for blue or distinction between blue and green. When shown a circle with 11 green squares and one blue, they couldn’t pick out which one was different from the others.

     

    When looking at a circle of green squares with only one slightly different shade, they could immediately spot the different one. Can you?

     

    Davidoff says that without a word for a colour, without a way of identifying it as different, it’s much harder for us to notice what’s unique about it — even though our eyes are physically seeing the blocks it in the same way.

     

    Further research brought to wider discussions about color perception in humans. Everything that we make is based on the fact that humans are trichromatic. The television only has 3 colors. Our color printers have 3 different colors. But some people, and in specific some women seemed to be more sensible to color differences… mainly because they’re just more aware or – because of the job that they do.

    Eventually this brought to the discovery of a small percentage of the population, referred to as tetrachromats, which developed an extra cone sensitivity to yellow, likely due to gene modifications.

    The interesting detail about these is that even between tetrachromats, only the ones that had a reason to develop, label and work with extra color sensitivity actually developed the ability to use their native skills.

     

    So before blue became a common concept, maybe humans saw it. But it seems they didn’t know they were seeing it.

    If you see something yet can’t see it, does it exist? Did colours come into existence over time? Not technically, but our ability to notice them… may have…

     

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  • Types of Film Lights and their efficiency – CRI, Color Temperature and Luminous Efficacy

    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

     

    Light source CCT (K) CRI
    Low-pressure sodium (LPS/SOX) 1800 −44
    Clear mercury-vapor 6410 17
    High-pressure sodium (HPS/SON) 2100 24
    Coated mercury-vapor 3600 49
    Halophosphate warm-white fluorescent 2940 51
    Halophosphate cool-white fluorescent 4230 64
    Tri-phosphor warm-white fluorescent 2940 73
    Halophosphate cool-daylight fluorescent 6430 76
    “White” SON 2700 82
    Standard LED Lamp 2700–5000 83
    Quartz metal halide 4200 85
    Tri-phosphor cool-white fluorescent 4080 89
    High-CRI LED lamp (blue LED) 2700–5000 95
    Ceramic discharge metal-halide lamp 5400 96
    Ultra-high-CRI LED lamp (violet LED) 2700–5000 99
    Incandescent/halogen bulb 3200 100

     

    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

    (more…)

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LIGHTING