Colour an algorithmic approach Thomas Bangert PhD Research Topic

part 1: The Colour Model The Munsell Colour Model actual mapping to human vision A colour catalog vs a colour model

color catalog vs colour model catalog requires selection of colours based on perceptual matching partial colour model codes spectrum as systematic mixing of wavelengths true colour model codes the color of spectrum X=100,Y=100,Z=0 Yellow

Colour as information a theory of information processing.

Colour Reproduction true colour code + specs of viewer = image –colour defined by code –viewer can be group or individual –display decides how to create colour from code –gives perceptual predictability Yellow Orange bluish-red Magenta +

The Standard Observer from colour matching studies CIE1931 xy chromaticity diagram primaries at: 435.8nm, 546.1nm, 700nm The XYZ sensor response Y is defined as luminance difference from Y is the colour information The Math: … 2-d as z is redundant

Understanding CIE chromaticity White in center Saturated / monochromatic wavelengths on the periphery Best understood as a failed colour circle Everything in between is a mix of white and the colour Circular colour models are the holy grail of colour theory … so far no one has succeeded! x and y show difference from Y

Does it match? Problem #1: ‘negative primaries’ But does the CIE model work? Problem #2: no definition of colour

Colour Sensor response to monochromatic light Human Bird 4 sensors Equidistant on spectrum What are these sensors used for? What information is needed? my answer is: Wavelength

How to calculate wavelength with 2 poor quality luminance sensors. Roughly speaking: Wavelength λ-Δλ-Δλ λ+Δλ+Δ R G a shift of Δ from a known reference point

the ideal light stimulus monochromatic stimulus Allows wavelength to be measured in relation to reference. Monochromacy: The reason we see rainbows is because the human visual system works with single wavelength light -- monochromatic light This is the underlying paradigm!

Problem: natural light is not ideal Light stimulus might not activate reference sensor fully. Light stimulus might not be fully monochromatic. ie. there might be white mixed in

Solution: A 3 rd sensor is used to measure equiluminance. Which is subtracted. Then reference sensor can be normalized Equiluminance & Normalization – essential to finding wavelength, can also called saturation and lightness

a 4 sensor design 2 opponent pairs only 1 of each pair can be active min sensor is equiluminance

Human Retina only has 3 sensors! What to do? We add an emulation layer. Hardware has 3 physical sensors but emulates 4 sensors No maths … just a diagram!

Testing Colour Opponent model What we should see What we do see There is Red in our Blue – the problem of Purple

Pigment Absorption Data of human cone sensors Red > Green

Dual Opponency with Circularity an ideal model using 2 sensor pairs

a circular colour model We divide colour coding and colour reproduction: Coding no need to link to specific observer – ideal observer not linked specifically to human vision Display decides how best to present colour to observer – making colour anomalies fit

Part 1 – Coding Colour fully circular universal ideal observer Part 2 – Reproducing Colour takes knowledge about observer and optimizes/distorts to the individual/group improved or natural reproduction modes

Coding Natural Colour Problem #1: Real world is not monochromatic Spectrum of a common yellow flower

Colour coding … for dual channel opponency Problem # 1 easy to solve we simply assume monochromacy when stimuli are not monochromatic opponent channels simply subtract to 0 green, yellow and red are active r-g = 0 b = 0 leaving only yellow stimuli equivalent to monochromatic

Opponent Coding Only primaries are true colours all other colours are intermediary … and can be generated by proportions of primaries!

Accurate colour reproduction … for humans Any colour may be displayed by a combination of 2 primaries but the location of primaries can vary between individuals and intermediary locations can be distorted Problem # 2

Accurate colour reproduction … tuned to the individual 1.primaries must be mapped for the individual 2.mid-points must be mapped Provides an individual colour profile … a map of the primaries and intermediary points.

tunable primaries Wavelength(nm) Blue Green Yellow Red

Part 3 testing the theory is it sound? is it useful? does human vision use it? Is there empirical evidence to support paradigm + theory? note: a theoretical model about information is the information itself!

Apparatus monochromator light source equal light across visible spectrum

the stimuli

Transition Colour Matching generate subject selectable monochromatic stimuli subject selects colour perceptual primaries are calculated

Results no leading questions -- only “blue” 4 primaries (pure colours) naturally resolve to blue, green, yellow and red primaries are equidistant transitions worked for all subjects Most subjects see peripheral colours –red in blue –40% could see “magenta” – blue in red potential problem: people treat purple as if it were primary some colour blind people can’t see purple

histogram of results

results from mapping colour vision

Application natural colour reproduction

Current display technology: 0.1 – 100 cd/m 2 (currently pushed up to 500, but designed for 100 cd/m 2 ) DICOM GSDF: 0.05 – 4,000 cd/m 2 (defined for grayscale medical imaging only) Natural environment: 0.01 – 10,000 cd/m 2 Luminance: High Dynamic Range

Coding HDR HDR is here now … using multiple exposure! … using an absolute lumiance code rather than a relative code

the colour of infra-red ( nm) remove the filter from a digital camera & it will work in the infra-red Images in the infra- red produced by enthusiasts now! What is the colour when you go beyond red? not the stereo-type but true infra-red – high wavelength light

related work:Dolby

Examples of real world colour? Colours are often computed, not measured!

… an extreme example What is the colour?

Poynton, C. A. (1995). “Poynton’s Color FAQ”, electronic preprint. Bangert, Thomas (2008). “TriangleVision: A Toy Visual System”, ICANN Goldsmith, Timothy H. (July 2006). “What birds see”. Scientific American: 69–75. Neitz, Jay; Neitz, Maureen. (August 2008). “Colour Vision: The Wonder of Hue”. Current Biology 18(16): R700-r Questions? References