IMCO: Processing Color

How a camera works

Human Eye vs a Camera

Shutter: how long the camera is opened to let in light

The imaging flow

The camera pipeline for creating nice photos is a secret recipe

The color Imaging Flow

Source

Other possible steps:

ISO Gain
Noise Reduction
Etc.

Sensor

Temporal Mutliplexing
- Scan 3 times + use 1 sensor
- 3 real values per pixel
- Only for static scenes
- Slow
Scan 1 time + Use 3 sensors
- 3 real values per pixel
- Costly
- Space
Spatial Multiplexing
- Scan 1 times + Use 1 sensor
- Well-mastered technology
- 1 real value per pixel (interpolation)
- Loss of light

Camera spectral sensitivity

Source

Sensor (from 110 years ago!)

Using temporal multiplexing

Preprocessing

Dark Current Compensation
- Signal present even when the length is close, which adds noise

How to compensate ?

Capture a dark image for the given exposure time

\[ C_i(x,y)=R_i(x,y) - D_i(x,y) \]

Vignetting/Lens Shading/Flat Field correction
- Image tends to darken on the corners/Non-uniform Illumination

How to compensate ?

You tell me !

Compute average RGB value of 15 White Patches

	Statistic	Red	Green	Blue
Before correction	Mean	157.3	159.1	157.5
After correction	Mean	249.5	\	\

If you are taking RAW images there is a chance your image software already does it

Color Constancy

Property of the Human Visual Sytem that allows adapting to different scene illuminations

White Balance to the rescue

The eye cares more about the intrinsic color of the object, not the color of the light leaving the object

Easy for our eyes to judge what is white under different illuminants, but not so straightforward to camera

White Balance

The idea is to make white points the same between scenes

White point is the

\(xyY\) (or \(XYZ\)) value of an ideal "white reference"

Hence, the camera is able to do the Chromatic Adaptation

Chromatic Adaptation: How ?

\(\color{yellow}{\text{Source}}\) color
- \((X_s, Y_s, Z_s)\) with white reference
  \((X_{WS}, Y_{WS}, Z_{WS})\)
\(\color{blue}{Target}\) color
- \((X_T,Y_T,Z_T)\) with white reference
  \((X_{WT}, Y_{WT}, Z_{WT})\)

Computing

\([M]\)

Transform
\(XYZ\) to cone response domain
Scale using source/target white references
Transform back form cone domain
\(XYZ\)

\[ [M] = [MA]^{-1}\begin{bmatrix} \frac{\rho_T}{\rho_S}&0&0\\ 0&\frac{\gamma_T}{\gamma_S}&0\\ 0&0&\frac{\beta_t}{\beta_s} \end{bmatrix} [MA] \]

Where

\(\rho, \gamma,\beta\) are the cone responses for the given source and target colors

Example

White balance: automatic

What if we don't help the camera ?

The camera doesn't know the illuminant

The camera will try to "guess" the white point in the image, and then balance the color automatically.

Unlike White Balance, the illuminant is very hard to estimate

How ?

Gray world assumption
- The average of all colors in the image is gray
- Green channel is taken as "gray" reference
- White-balanced image is:
  \(k_{r}R,G,k_bB\)
  - \(k_r=\frac{G_{mean}}{R_{mean}}\)
  - \(k_b = \frac{G_{mean}}{B_{mean}}\)
White patch algorithm
- Assumes that highlights = specular reflections of illuminant
- Maximum
  \(R,G,B\) values are good estimation of white point
- White balanced image is:
  \(k_{r}R,G,k_bB\)
  - \(k_r=\frac{G_{max}}{R_{max}}\)
  - \(k_b = \frac{G_{max}}{B_{max}}\)
How does GIMP does it ?
1. Discards pixels colors at the extremes of
  \(R,G,B\) histograms
  - Thresholds of
    \(0.05\%\) of total pixel count
2. Do Histogram Stretching of remaining pixel colors, for each channel
3. Plus some smart post-processing
Smart Color Balance method
1. Discards "black" and "white" pixels
2. Stretch the histogram of the remaining pixels
3. Plus some smart post-processing

Those are very basic algorithms.

Modern cameras use sophisticated white-balance, based on data-driven solutions

One Way
- Compute features from image
- Find similar images in databases of images with good white balance
- Use white balance from image

Demosaic

Each pixel has a different color filter
Bayer Pattern is the most used Color Filter Array (CFA)
Why More Green ?
- Frequency of the G color band is close to the peak of the human luminance frequency response
  \(\to\) better sharpness

Linear interpolation

Average of neighors
Smoother kernels (bicubic) can also be used

Typical error

Taking a picture of striped pattern

\(\to\) color artifact

To overcome these problems, most demosaicing methods convert the image to the YCbCr

Solution

Each pixel has a different color filter
Bayer Pattern is the moste used Color Filter Array (CFA)
Interpolation methods are patented or proprietary
And of course deep learning to the rescue

Color Transform
\(RGB_c\to RGB_u\)

Color Correction

Cameras are meant to produce pleasing scenes rather than colorimetrically accurate scenes
- Spectral Sensitivities of the camera are not identical to human color matching functions
To obtain colorimetric accuracy, we need to transform the image from the sensor's color space to the colorimetrics
\((XYZ)\) space
By default Factory-computed Color Space Transforms (CST) are used
When precision is needed, Color Charts are used to obtain a specific transform for the camera and scene

We can use ICC color profile

Color Correction Charts

ColorChecker Digital SG (SG=Semi-gloss)

140 patches (96 uniques colors)

Artist Paint Target

ColorBuild 300 Patch Target

Color Correction Methods

\(30+\) years of continous research on how to transform form RGB to XYZ spaces
- A lot of ways to do it !
Most of them solve the following equation:

\[ X=MP \]

\(X\): Reference
\(XYZ\) values
\([3\times m]\)
\(P\): Camera
\(RGB\) values
\([N\times m]\)
\(M\): Correction matrix
\([3\times N]\)
\(m\): nb of data points
\(N\): nb of dimensions

	ColorChecker Classic	ColorChecker Digital SG
m	24	140

Linear

Linear Mapping from

\(RGB\) to \(XYZ\)

\(N=3\)
Not affected by exposure change

Polynomial Color Correction

Linear Mapping From

\(RGB\) to \(XYZ\) + add polynomial components to reduce errors

\(N=9\), if
\(2^{nd}\) order polynomial
Affected by exposure changes
High polynomial degree tends to do overfitting

Root Polynomial Color Correction