COLOR

  • The Color of Infinite Temperature

    This is the color of something infinitely hot.

    Of course you’d instantly be fried by gamma rays of arbitrarily high frequency, but this would be its spectrum in the visible range.

    johncarlosbaez.wordpress.com/2022/01/16/the-color-of-infinite-temperature/

    This is also the color of a typical neutron star. They’re so hot they look the same.
    It’s also the color of the early Universe!

    This was worked out by David Madore.

    The color he got is sRGB(148,177,255).
    www.htmlcsscolor.com/hex/94B1FF

    And according to the experts who sip latte all day and make up names for colors, this color is called ‘Perano’.

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  • 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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  • Black Body color aka the Planckian Locus curve for white point eye perception

    http://en.wikipedia.org/wiki/Black-body_radiation

     

    Black-body radiation is the type of electromagnetic radiation within or surrounding a body in thermodynamic equilibrium with its environment, or emitted by a black body (an opaque and non-reflective body) held at constant, uniform temperature. The radiation has a specific spectrum and intensity that depends only on the temperature of the body.

     

    A black-body at room temperature appears black, as most of the energy it radiates is infra-red and cannot be perceived by the human eye. At higher temperatures, black bodies glow with increasing intensity and colors that range from dull red to blindingly brilliant blue-white as the temperature increases.

    The Black Body Ultraviolet Catastrophe Experiment

     

    In photography, color temperature describes the spectrum of light which is radiated from a “blackbody” with that surface temperature. A blackbody is an object which absorbs all incident light — neither reflecting it nor allowing it to pass through.

     

    The Sun closely approximates a black-body radiator. Another rough analogue of blackbody radiation in our day to day experience might be in heating a metal or stone: these are said to become “red hot” when they attain one temperature, and then “white hot” for even higher temperatures. Similarly, black bodies at different temperatures also have varying color temperatures of “white light.”

     

    Despite its name, light which may appear white does not necessarily contain an even distribution of colors across the visible spectrum.

     

    Although planets and stars are neither in thermal equilibrium with their surroundings nor perfect black bodies, black-body radiation is used as a first approximation for the energy they emit. Black holes are near-perfect black bodies, and it is believed that they emit black-body radiation (called Hawking radiation), with a temperature that depends on the mass of the hole.

     

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  • 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

    (more…)

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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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  • What light is best to illuminate gems for resale

    www.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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