COLOR

  • Photography basics: Color Temperature and White Balance

     

     

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

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

     

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

     

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

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

     

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

     

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

     

     

    Details in the post

    (more…)

    , , ,
    Read more: Photography basics: Color Temperature and White Balance
  • 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.

     

     

     

    , ,
    Read more: Photography Basics : Spectral Sensitivity Estimation Without a Camera
  • Capturing textures albedo

    Building a Portable PBR Texture Scanner by Stephane Lb
    http://rtgfx.com/pbr-texture-scanner/

     

     

    How To Split Specular And Diffuse In Real Images, by John Hable
    http://filmicworlds.com/blog/how-to-split-specular-and-diffuse-in-real-images/

     

    Capturing albedo using a Spectralon
    https://www.activision.com/cdn/research/Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf

    Real_World_Measurements_for_Call_of_Duty_Advanced_Warfare.pdf

    Spectralon is a teflon-based pressed powderthat comes closest to being a pure Lambertian diffuse material that reflects 100% of all light. If we take an HDR photograph of the Spectralon alongside the material to be measured, we can derive thediffuse albedo of that material.

     

    The process to capture diffuse reflectance is very similar to the one outlined by Hable.

     

    1. We put a linear polarizing filter in front of the camera lens and a second linear polarizing filterin front of a modeling light or a flash such that the two filters are oriented perpendicular to eachother, i.e. cross polarized.

     

    2. We place Spectralon close to and parallel with the material we are capturing and take brack-eted shots of the setup7. Typically, we’ll take nine photographs, from -4EV to +4EV in 1EVincrements.

     

    3. We convert the bracketed shots to a linear HDR image. We found that many HDR packagesdo not produce an HDR image in which the pixel values are linear. PTGui is an example of apackage which does generate a linear HDR image. At this point, because of the cross polarization,the image is one of surface diffuse response.

     

    4. We open the file in Photoshop and normalize the image by color picking the Spectralon, filling anew layer with that color and setting that layer to “Divide”. This sets the Spectralon to 1 in theimage. All other color values are relative to this so we can consider them as diffuse albedo.

    , , ,
    Read more: Capturing textures albedo
  • About green screens

    hackaday.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: #009933

     

    Chroma 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.

    , ,
    Read more: About green screens

LIGHTING

Collections
| Explore posts
| Design And Composition
| Featured AI

Popular Searches
unreal | pipeline | virtual production | free | learn | photoshop | 360 | macro | google | nvidia | resolution | open source | hdri | real-time | photography basics | nuke





Subscribe to PixelSham.com RSS for free
Subscribe to PixelSham.com RSS for free