Estimating information from image colors: An application to digital cameras and natural scenes

Iván Marín-Franch, David H. Foster

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    Abstract

    The colors present in an image of a scene provide information about its constituent elements. But the amount of information depends on the imaging conditions and on how information is calculated. This work had two aims. The first was to derive explicitly estimators of the information available and the information retrieved from the color values at each point in images of a scene under different illuminations. The second was to apply these estimators to simulations of images obtained with five sets of sensors used in digital cameras and with the cone photoreceptors of the human eye. Estimates were obtained for 50 hyperspectral images of natural scenes under daylight illuminants with correlated color temperatures 4,000, 6,500, and 25,000 K. Depending on the sensor set, the mean estimated information available across images with the largest illumination difference varied from 15.5 to 18.0 bits and the mean estimated information retrieved after optimal linear processing varied from 13.2 to 15.5 bits (each about 85 percent of the corresponding information available). With the best sensor set, 390 percent more points could be identified per scene than with the worst. Capturing scene information from image colors depends crucially on the choice of camera sensors. © 1979-2012 IEEE.
    Original languageEnglish
    Article number6175904
    Pages (from-to)78-91
    Number of pages13
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume35
    Issue number1
    DOIs
    Publication statusPublished - 2013

    Keywords

    • color constancy
    • color information
    • color processing
    • Color vision
    • digital color cameras
    • information theory
    • kth-nearest-neighbor statistics
    • natural scenes

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