To avoid the ambiguity of the term pixel – does it constitute one component or three? – I suggest that you call a sensor element a photosite.

In a “one-chip” camera, hardware or firmware performs spatial interpolation to reconstruct R, G, and B at each photosite. In a “three-chip” camera, dichroic filters are mounted on one or two glass blocks. In optical engineering, a glass block is called a prism, but it is not the prism that separates the wavelengths, it is the dichroic filters.

Kuniba, Hideyasu and Roy S. Berns,

(2009), “Spectral sensitivity optimization of color image sensors considering photon shot noise,” in Journal of Electronic Imaging 18 (2):

023002-1–023002-14.

Figure 26.3, on page 300

Figure 26.4, on page 301

Figure 26.5, on page 302

Figure 26.6, on page 303

Figure 26.7, on page 304

Figure 26.8, on page 305

Every colour video camera or digital still camera needs to sense the image through three different spectral characteristics. Digital still cameras and consumer camcorders typically have a single area array CCD sensor (“one chip”); each 2× 2 tile of the array has sensor elements covered by three different types of filter. Typically, filters appearing red, green, and blue are used; the green filter is duplicated onto two of the photosites in the 2× 2 tile. This approach loses light, and therefore sensitivity. Conventional studio video cameras separate incoming light using dichroic filters operating as beam splitters; each component has a dedicated CCD sensor (“3 CCD,” or “3 CMOS”). Such an optical system separates different wavelength bands without absorbing any light, achieving about a factor of two higher sensitivity than a mosaic sensor.

Figure 26.7 shows the set of spectral sensitivity functions implemented by the beam splitter and filter (“prism”) assembly of an actual HD camera. The functions are positive everywhere across the spectrum, so the filters are physically realizable. However, rather poor colour reproduction will result if these signals are used directly to drive a display having BT.709 primaries. Figure 26.8 shows the same set of camera analysis functions processed through a 3× 3 matrix transform. The transformed components will reproduce colour more accurately – the more closely these curves resemble the ideal BT.709 CMFs of Figure 26.5, the more accurate the camera’s colour reproduction will be.

In theory, and in practice, using a linear matrix to process the camera signals can capture all colours correctly. However, capturing all colours is seldom necessary in practice, as I will explain in the Gamut section below. Also, capturing the entire range of colours would incur a noise penalty, as I will describe in

Noise due to matrixing, on page 308.

Normalization and scaling

_

The y(λ) CMF is standardized by the CIE such that its

maximum value lies at unity. For the 10 nm CIE CMFs

_

commonly used in image science, the y curve integrates

_ _

to about 10.68. The x(λ) and z(λ) CMFs are scaled such that they integrate to the same value. The CIE derived its 10 nm CMFs by interpolation from its 1 nm curves;

SPD of red primary

SPD of green primary

SPD of blue primary

2.0

1.0

0.0

400

–1.0

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400

–1.0

2.0

1.0

0.0

400

Wavelength [nm]

RSR of red sensor

RSR of green sensor

RSR of blue sensor

2.0

1.0

0.0

400

–1.0

2.0

1.0

0.0

400

–1.0

2.0

1.0

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400

Wavelength [nm]

Matrixed sensitivity of green Matrixed sensitivity of red

Matrixed sensitivity of blue

2.0

1.0

0.0

–1.0

2.0

1.0

0.0

–1.0

2.0

1.0

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400

400

400

Wavelength [nm]