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Computer Vision Metrics: Chapter Two (Part C)

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For Part B of Chapter Two, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


Practical Considerations for Color Enhancements

For image pre-processing, the color intensity is usually the only color information that should be enhanced, since the color intensity alone carries a lot of information and is commonly used. In addition, color processing cannot be easily done in RGB space while preserving relative color. For example, enhancing the RGB channels independently with a sharpen filter will lead to Moiré fringe artifacts when the RGB channels are recombined into a single rendering. So to sharpen the image, first forward-convert RGB to a color space such as HSV or YIQ, then sharpen the V or Y component, and then inverse-convert back to RGB. For example, to correct illumination in color, standard image processing methods such as LUT remap or histogram equalization will work, provided they are performed in the intensity space.

As a practical matter, for quick color conversions to gray scale from RGB, here are a few methods. (1) The G color channel is a good proxy for gray scale information, since as shown in the sensor discussion in Chapter 1, the RB wavelengths in the spectrum overlap heavily into the G wavelengths. (2) Simple conversion from RGB into gray scale intensity I can be done by taking I = R+G+B / 3. (3) The YIQ color space, used in the NTSC television broadcast standards, provides a simple forward/backward method of color conversion between RGB and a gray scale component Y, as follows:

Color Accuracy and Precision

If color accuracy is important, 8 bits per RGB color channel may not be enough. It is necessary to study the image sensor vendor’s data sheets to understand how good the sensor really is. At the time of this writing, common image sensors are producing 10 to 14 bits of color information per RGB channel. Each color channel may have a different spectral response, as discussed in Chapter 1.

Typically, green is a good and fairly accurate color channel on most devices; red is usually good as well and may also have near infrared sensitivity if the IR filter is removed from the sensor; and blue is always a challenge since the blue wavelength can be hardest to capture in smaller silicon wells, which are close to the size of the blue wavelength, so the sensor...