COLOR

  • HDR and Color

    https://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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  • Eye retina’s Bipolar Cells, Horizontal Cells, and Photoreceptors

    In the retina, photoreceptors, bipolar cells, and horizontal cells work together to process visual information before it reaches the brain. Here’s how each cell type contributes to vision:

     

    1. Photoreceptors

    • Types: There are two main types of photoreceptors: rods and cones.
      • Rods: Specialized for low-light and peripheral vision; they help us see in dim lighting and detect motion.
      • Cones: Specialized for color and detail; they function best in bright light and are concentrated in the central retina (the fovea), allowing for high-resolution vision.
    • Function: Photoreceptors convert light into electrical signals. When light hits the retina, photoreceptors undergo a chemical change, triggering an electrical response that initiates the visual process. Rods and cones detect different intensities and colors, providing the foundation for brightness and color perception.

     

    2. Bipolar Cells

    • Function: Bipolar cells act as intermediaries, connecting photoreceptors to ganglion cells, which send signals to the brain. They receive input from photoreceptors and relay it to the retinal ganglion cells.
    • On and Off Bipolar Cells: Some bipolar cells are ON cells, responding when light is detected (depolarizing in light), and others are OFF cells, responding in darkness (depolarizing in the absence of light). This division allows for more precise contrast detection and the ability to distinguish light from dark areas in the visual field.

     

    3. Horizontal Cells

    • Function: Horizontal cells connect photoreceptors to each other and create lateral interactions between them. They integrate signals from multiple photoreceptors, allowing them to adjust the sensitivity of neighboring photoreceptors in response to varying light conditions.
    • Lateral Inhibition: This process improves visual contrast and sharpness by making the borders between light and dark areas more distinct, enhancing our ability to perceive edges and fine detail.

     

    These three types of cells work together to help the retina preprocess visual information and perception, emphasizing contrast and adjusting for different lighting conditions before signals are sent to the brain for further processing and interpretation.

     

     

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    Read more: Eye retina’s Bipolar Cells, Horizontal Cells, and Photoreceptors
  • 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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