Tag Archives: luminance

Angles and the Camera Equation

Imagine a bucolic scene on a clear sunny day at the equator, sand warmed by the tropical sun with a typical irradiance (E) of about 1000 watts per square meter.  As discussed earlier we could express this quantity as illuminance in lumens per square meter (lx) – or as a certain number of photons per second (\Phi) over an area of interest (\mathcal{A}).

(1)   \begin{equation*} E = \frac{\Phi}{\mathcal{A}}  \; (W, lm, photons/s) / m^2 \end{equation*}

How many photons/s per unit area can we expect on the camera’s image plane (irradiance E_i )?

Figure 1.  Irradiation transfer from scene to sensor.

In answering this question we will discover the Camera Equation as a function of opening angles – and set the stage for the next article on lens pupils.  By the way, all quantities in this article depend on wavelength and position in the Field of View, which will be assumed in the formulas to make them readable, see Appendix I for a more formally correct version of Equation (1).

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Off Balance

In this article we confirm quantitatively that getting the White Point, hence the White Balance, right is essential to obtaining natural tones out of our captures.  How quickly do colors degrade if the estimated Correlated Color Temperature is off?

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A Question of Balance

In this article I bring together qualitatively the main concepts discussed in the series and argue that in many (most) cases a  photographer’s job in order to obtain natural looking tones in their work during raw conversion is to get the illuminant and relative white balance right – and to step away from any slider found in menus with the word ‘color’ in them.

Figure 1. DON’T touch them color dials (including Tint)! courtesy of Capture One

If you are an outdoor photographer trying to get balanced greens under an overcast sky – or a portrait photographer after good skin tones – dialing in the appropriate scene, illuminant and white balance puts the camera/converter manufacturer’s color science to work and gets you most of the way there safely.  Of course the judicious photographer always knew to do that – hopefully now with a better appreciation as for why.

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White Point, CCT and Tint

As we have seen in the previous post, knowing the characteristics of light at the scene is critical to be able to determine the color transform that will allow captured raw data to be naturally displayed from an output color space like ubiquitous sRGB.

White Point

The light source Spectral Power Distribution (SPD) corresponds to a unique White Point, namely a set of coordinates in the XYZ color space, obtained by multiplying wavelength-by-wavelength its SPD (the blue curve below) by the Color Matching Functions of a Standard Observer (\hat{x},\hat{y},\hat{z})

Figure 1.  Spectral Power Distribution of Standard Daylight Illuminant D5300 with a Correlated Color Temperature of  5300 deg. K; and CIE (2012) 2-deg XYZ “physiologically relevant” Color Matching Functions from cvrl.org.

Adding (integrating) the three resulting curves up we get three values that represent the illuminant’s coordinates in the XYZ color space.  The White Point is then obtained by dividing these coordinates by the Y value to normalize it to 1.

The White Point is then seen to be independent of the intensity of the arriving light, as Y represents Luminance from the scene.   For instance a Standard Daylight Illuminant with a Correlated Color Temperature of 5300k has a White Point of[1]

XYZn = [0.9593 1.0000 0.8833] Continue reading White Point, CCT and Tint

Linear Color Transforms

Building on a preceeding article of this series, once demosaiced raw data from a Bayer Color Filter Array sensor represents the captured image as a set of triplets, corresponding to the estimated light intensity at a given pixel under each of the three spectral filters part of the CFA.   The filters are band-pass and named for the representative peak wavelength that they let through, typically red, green, blue or r, g, b for short.

Since the resulting intensities are linearly independent they can form the basis of a 3D coordinate system, with each rgb triplet representing a point within it.  The system is bounded in the raw data by the extent of the Analog to Digital Converter, with all three channels spanning the same range, from Black Level with no light to clipping with maximum recordable light.  Therefore it can be thought to represent a space in the form of a cube – or better, a parallelepiped – with the origin at [0,0,0] and the opposite vertex at the clipping value in Data Numbers, expressed as [1,1,1] if we normalize all data by it.

Figure 1. The linear sRGB Cube, courtesy of Matlab toolbox Optprop.

The job of the color transform is to project demosaiced raw data rgb to a standard output RGB color space designed for viewing.   Such spaces have names like sRGB, Adobe RGB or Rec. 2020 .  The output space can also be shown in 3D as a parallelepiped with the origin at [0,0,0] with no light and the opposite vertex at [1,1,1] with maximum displayable light. Continue reading Linear Color Transforms

Cone Fundamentals & the LMS Color Space

In the last article we showed how a digital camera’s captured raw data is related to Color Science.  In my next trick I will show that CIE 2012 2 deg XYZ Color Matching Functions \bar{x}, \bar{y}, \bar{z} displayed in Figure 1 are an exact linear transform of Stockman & Sharpe (2000) 2 deg Cone Fundamentals \bar{\rho}, \bar{\gamma}, \bar{\beta} displayed in Figure 2

(1)   \begin{equation*} \left[ \begin{array}{c} \bar{x}} \\ \bar{y} \\ \bar{z} \end{array} \right] = M_{lx} * \left[ \begin{array} {c}\bar{\rho} \\ \bar{\gamma} \\ \bar{\beta} \end{array} \right] \end{equation*}

with CMFs and CFs in 3xN format, M_{lx} a 3×3 matrix and * matrix multiplication.  Et voilà:[1]

Figure 1.  Solid lines: CIE (2012) 2° XYZ “physiologically-relevant” Colour Matching Functions and photopic Luminous Efficiency Function (V) from cvrl.org, the Colour & Vision Research Laboratory at UCL.  Dotted lines: The Cone Fundamentals in Figure 2 after linear transformation by 3×3 matrix Mlx below.  Source: cvrl.org.

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Connecting Photographic Raw Data to Tristimulus Color Science

Absolute Raw Data

In the previous article we determined that the three r_{_L}g_{_L}b_{_L} values recorded in the raw data in the center of the image plane in units of Data Numbers per pixel – by a digital camera and lens as a function of absolute spectral radiance L(\lambda) at the lens – can be estimated as follows:

(1)   \begin{equation*} r_{_L}g_{_L}b_{_L} =\frac{\pi p^2 t}{4N^2} \int\limits_{380}^{780}L(\lambda) \odot SSF_{rgb}(\lambda)  d\lambda \end{equation*}

with subscript _L indicating absolute-referred units and SSF_{rgb} the three system Spectral Sensitivity Functions.   In this series of articles \odot is wavelength by wavelength multiplication (what happens to the spectrum of light as it progresses through the imaging system) and the integral just means the area under each of the three resulting curves (integration is what the pixels do during exposure).  Together they represent an inner or dot product.  All variables in front of the integral were previously described and can be considered constant for a given photographic setup. Continue reading Connecting Photographic Raw Data to Tristimulus Color Science

The Physical Units of Raw Data

In the previous article we (I) learned that the Spectral Sensitivity Functions of a given digital camera and lens are the result of the interaction of light from the scene with all of the spectrally varied components that make up the imaging system: mainly the lens, ultraviolet/infrared hot mirror, Color Filter Array and other filters, finally the photoelectric layer of the sensor, which is normally silicon in consumer kit.

Figure 1. The journey of light from source to sensor.  Cone Ω will play a starring role in the narrative that follows.

In this one we will put the process on a more formal theoretical footing, setting the stage for the next few on the role of white balance.

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The Spectral Response of Digital Cameras

Photography works because visible light from one or more sources reaches the scene and is reflected in the direction of the camera, which then captures a signal proportional to it.  The journey of light can be described in integrated units of power all the way to the sensor, for instance so many watts per square meter. However ever since Newton we have known that such total power is in fact the result of the weighted sum of contributions by every frequency  that makes up the light, what he called its spectrum.

Our ability to see and record color depends on knowing the distribution of the power contained within a subset of these frequencies and how it interacts with the various objects in its path.  This article is about how a typical digital camera for photographers interacts with the spectrum arriving from the scene: we will dissect what is sometimes referred to as the system’s Spectral Response or Sensitivity.

Figure 1. Spectral Sensitivity Functions of an arbitrary imaging system, resulting from combining the responses of the various components described in the article.

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Bayer CFA Effect on Sharpness

In this article we shall find that the effect of a Bayer CFA on the spatial frequencies and hence the ‘sharpness’ information captured by a sensor compared to those from the corresponding monochrome version can go from (almost) nothing to halving the potentially unaliased range – based on the chrominance content of the image and the direction in which the spatial frequencies are being stressed. Continue reading Bayer CFA Effect on Sharpness

COMBINING BAYER CFA MTF Curves – II

In this and the previous article I discuss how Modulation Transfer Functions (MTF) obtained from every color channel of a Bayer CFA raw capture in isolation can be combined to provide a meaningful composite MTF curve for the imaging system as a whole.

There are two ways that this can be accomplished: an input-referred approach (L) that reflects the performance of the hardware only; and an output-referred one (Y) that also takes into consideration how the image will be displayed.  Both are valid and differences are typically minor, though the weights of the latter are scene, camera/lens, illuminant dependent – while the former are not.  Therefore my recommendation in this context is to stick with input-referred weights when comparing cameras and lenses.1 Continue reading COMBINING BAYER CFA MTF Curves – II

Photons Emitted by Light Source

How many photons are emitted by a light source? To answer this question we need to evaluate the following simple formula at every wavelength in the spectral range of interest and add the values up:

(1)   \begin{equation*} \frac{\text{Power of Light in }W/m^2}{\text{Energy of Average Photon in }J/photon} \end{equation*}

The Power of Light emitted in W/m^2 is called Spectral Exitance, with the symbol M_e(\lambda) when referred to  units of energy.  The energy of one photon at a given wavelength is

(2)   \begin{equation*} e_{ph}(\lambda) = \frac{hc}{\lambda}\text{    joules/photon} \end{equation*}

with \lambda the wavelength of light in meters and h and c Planck’s constant and the speed of light in the chosen medium respectively.  Since Watts are joules per second the units of (1) are therefore photons/m^2/s.  Writing it more formally:

(3)   \begin{equation*} M_{ph} = \int\limits_{\lambda_1}^{\lambda_2} \frac{M_e(\lambda)\cdot \lambda \cdot d\lambda}{hc} \text{  $\frac{photons}{m^2\cdot s}$} \end{equation*}

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What Is Exposure

When capturing a typical photograph, light from one or more sources is reflected from the scene, reaches the lens, goes through it and eventually hits the sensing plane.

In photography Exposure is the quantity of visible light per unit area incident on the image plane during the time that it is exposed to the scene.  Exposure is intuitively proportional to Luminance from the scene $L$ and exposure time $t$.  It is inversely proportional to lens f-number $N$ squared because it determines the relative size of the cone of light captured from the scene.  You can read more about the theory in the article on angles and the Camera Equation.

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