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Color Primary Correction of Image and Video Between Different Source and Destination Color Spaces

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Abstract

This article presents an introductory review of color correction—a color remapping of image and video between different source and destination color spaces. The review specifically focuses on two main aspects of color remapping—primary color space conversion and gamut mapping—and outlines the requirements, algorithms, methods, and possible implementation options.

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Notes

  1. 1.

    Color perception is three-dimensional. In an RGB camera, or on a computer monitor, those attributes are the intensity of Red (R), Green (G), and Blue (B); in the Munsell color system, the attributes are Lightness, Saturation, and Hue; in the CIELAB system, the three coordinates are L*, a*, and b*. Thus, in any color space, there are three [2]. It is important to note, however, that cones actually do not detect any specific color, but respond to Long (L), Medium (M), and Short (S) wavelengths; LMS can be thought of as RGB.

  2. 2.

    sRGB is a normalized reference standard designed to match the color performance of an output device, e.g., a CRT monitor under typical viewing conditions.

  3. 3.

    Note that some manufacturers achieve cost downs and/or a higher light output for mobile displays by producing non-standard displays (i.e., displays with non-standard specifications).

  4. 4.

    The term tristimulus comes from the fact that color perception results from the retina of the eye responding to three types of stimuli.

  5. 5.

    A spectral color is the color sensation created by a monochromatic (single wavelength) light in the visible spectrum.

  6. 6.

    White point [10] is the tristimulus values or chromaticity coordinates that define a chosen color of “white,” often D65 [11].

  7. 7.

    These filters selectively pass light of a narrow range of colors and reflect all other colors.

  8. 8.

    Note that RGB is a color model and the range of colors that can be represented by the color model is its corresponding color space. Slightly different primaries (i.e., primaries with slightly different chromaticities) within the same RGB color model can give rise to different RGB color spaces. In this article, however, the terms model and space are used somewhat interchangeably.

  9. 9.

    Linearity allows saturation to be defined in terms of additive color mixing in (X,Y,Z) or any (R,G,B). Note that both the photopic luminance sensitivity (i.e., how light gray is) and the luminous sensitivity of the human eye (i.e., how light a color is) are linear.

  10. 10.

    The RGB values here are assumed to be in the range from 0 to 1, which is merely a convenience, and typical of a 100% filled gamut, but not essential.

  11. 11.

    The subscripts s and w refer to source and white, respectively.

  12. 12.

    x sr, in the matrix, denotes the x chromaticity (coordinate) of the red (r) primary and corresponds to the source (s) color space.

  13. 13.

    In this sense, the matrix C functions as a primary color-space conversion matrix.

  14. 14.

    The gain is actually applied to R′, G′, and B′—the non-linear gamma-corrected (following color space conversion and gamut mapping) R, G, and B signals that are sent to the display.

  15. 15.

    Implies a calculated output RGB value where R < 0 or G < 0 or B < 0.

  16. 16.

    Given the matrix coefficients, B in can be any value. So, the observation holds for green (B in = 0) and cyan (B in ≠ 0) inputs.

  17. 17.

    Once again, B in can be any value. So, the observation holds for red (B in = 0) and magenta (B in ≠ 0) inputs.

  18. 18.

    Recall that the RGB values are positive inside the destination gamut triangle, and negative outside.

  19. 19.

    Gamut mapping also goes by the name of gamut compression or gamut reduction.

  20. 20.

    Also, this 2D illustration gives no hint on what to do with the third dimension—lightness—of colors.

  21. 21.

    Tone mapping from HDR to SDR, and gamut mapping for MAX (R, G, B) > 1 are essentially the same problem.

  22. 22.

    Trying to invert a previous gamut mapping operation, without having metadata available that characterizes the operation, is guesswork. Luckily, there is now a trend towards invertible tone mapping and gamut mapping, both described by metadata that is transmitted with the down-mapped images.

  23. 23.

    Non-realizable in reality, but mathematically very real, e.g., (x, y) = (1, 0), (0, 1), and (0, 0)

  24. 24.

    One step further than perceptually uniform color spaces is to introduce (near-)straight lines of constant perceived hue, leading to color appearance models [41].

  25. 25.

    Note that these color spaces are good for quantitative analysis and color difference metrics, but not for image processing.

  26. 26.

    There are four steps if the conversions to and from the linear-light domain are counted.

  27. 27.

    The camera (that captured the scene) does not necessarily have a fundamental gamut limitation, but the grading display does. Therefore, the limitation actually stems from the gamut-mapping processing at the source because content producers like to see the colors that they sign off on; so they make the gamut of the content smaller or equal to the gamut of the grading display so that the display is faithful.

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Acknowledgment

The author is grateful to Jeroen Stessen for valuable and insightful discussions over the years that have led to many of the explanations presented in this article. Jeroen has been extremely gracious in proof reading of the material presented, and in permitting usage of diagrams from his 2007 presentation on extended color gamut at the HPA Tech Retreat [12]. At the time of the collaboration, Jeroen was a Senior Scientist at Philips Group Innovation, Intellectual Property & Standards, Eindhoven, Netherlands; he is currently a Principal Engineer at V-Silicon.

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  1. NVIDIA, Santa Clara, CA, USA

    Santanu Dutta

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  1. Santanu Dutta
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Editors and Affiliations

  1. Dept. of ECE and UMIACS, University of Maryland, College Park, MD, USA

    Shuvra S. Bhattacharyya

  2. Dept. of Computer Science, University of California, Los Angeles, CA, USA

    Miodrag Potkonjak

  3. Dept. of EECS, Syracuse University, Syracuse, NY, USA

    Senem Velipasalar

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Dutta, S. (2020). Color Primary Correction of Image and Video Between Different Source and Destination Color Spaces. In: Bhattacharyya, S., Potkonjak, M., Velipasalar, S. (eds) Embedded, Cyber-Physical, and IoT Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-16949-7_2

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