TU Delft. Digital color imaging & Digital color image processing. TU Delft. TU Delft. TU Delft. The human eye. Spectrum and Color I
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1 Digial color imaging & Digial color image processing The human eye Lucas J. van Vlie TNW: Faculy of Applied Sciences IST: Imaging Science & Technology PH: Digial Color Imaging 3 Specrum and Color I Single chip color CCD Visible sunligh consiss of a coninuous specrum of colors ranging from viole o red. The visible wavelenghs range from jus below 400 nm (viole) o well over 700 nm (red). Digial Color Imaging 2 Digial Color Imaging 4
2 Specrum and Color II A balanced (i.e. fla) specrum appears whie o he human observer. An unbalanced specrum exhibis some shade of color o he human observer. A human observer perceives color hrough he simuli of hree differen pigmened cones in he human eye. These cones are shorly denoed red, green and blue (or long, medium and shor). Absorpion of ligh by he red, green and blue cones in he human eye as a funcion of wavelengh. Color Maching Experimens For ri-chromacy we choose hree primaries, P = [ p 1, p 2, p 3 ] colorimeric independence if: Sp, Sp, Sp are linear independen A color mach is achieved or any hree-vecor a(f) ha saisfies The maching of all N monochromaic specra {e i } yields SPaf ()= Sf Wavelengh (nm) Two (differen) specra ha produce exacly he same responses of he cones, i.e. he risimuli, are said o have he same color and are indisinguishable for he human observer. Achromaic (void of color) ligh s only aribue is inensiy. Digial Color Imaging 5 SPai = Sei i = 1,..., N SPA = SI wih A (Nx3) he color-maching marix for he primaries P A = SPS ( ) 1 Digial Color Imaging 7 Trichromacy and Human Color Vision The response o he hree cones can be modeled by a hree-vecor c, wih λ max λmin c = s ( λ) f( λ) dλ i = 1,2,3 i Afer sampling he specra (10 nm) we ge in marix noaion i Addiive color model A weighed sum of primaries produces a color ha canno be disinguished by a sandard observer from he color of a specrum f. wih c= S f c (3x1): he cone response vecor, S (Nx3): sampled version of he specral sensiiviy funcions s i (λ), S = [s 1 s 2 s 3 ] f (Nx1): sampled version of he specrum f(λ). Two differen specra, represened by differen N-vecors, f and g produce he same response vecor and herefore represen he same color if λ Weighed sum of primaries SPaf ()= Sf Sandard observer Sf= Sg Digial Color Imaging 6 Digial Color Imaging 8
3 CIE RGB CMF s CIE RGB Color Maching Funcions (CMF s) are defined experimenally. Primaries are monochromaic sources a: nm, nm, nm wih radian inensiies ha yield equal ri-simulus for an equi-energy specrum. CMF s: r( λ), g( λ), b( λ) can become negaive! How can we ge negaive inensiies in color maching experimens? Sandard Illuminans The observed specra from (non-luninous), diffuse reflecing objecs depend heavily on he illuminan and characerized by he refleciviy vecor r (0<r i <1) The CIE XYZ risimus values are: = A Lr = A L r b( λ) g( λ) r ( λ)? wih A he marix of CIE XYZ CMF s, L he diagonal illuminan marix, and A L he visual subspace of L. D65 D50 A D65: day ligh (blue sky) D50: day ligh (clouded sky) A: incandescen lamp Digial Color Imaging 9 Digial Color Imaging 11 CIE XYZ CMF s CMF s: x( λ), y( λ), z( λ) are linear ransformaions of CIE RGB CMF s Conrains: non-negaive specra, y( λ) should be coinciden wih he luminous efficiency funcion normalizaion: equal ri-simulus values for an equi-energy specrum z( λ) Chromaiciy coordinaes CIE XYZ chromaiciy coordinaes: X x = X + Y + Z Y y = X + Y + Z y( λ ) x( λ) z= 1 x y All visible monochromaic specra appear on he horse-shoe. All mixures appear inside Digial Color Imaging 10 Digial Color Imaging 12
4 Transformaion of primaries HSI color model I Sandardized ses of RGB primaries appear in TV ses (NTSC / PAL / HDTV) Assume primaries Q and corresponding CMF s B 1 B = ( A Q) A HSI is suiable for describing he colors in he RGB cube Hue: describes a pure color (red-magena-blue-cyan-green-yellow-red) Sauraion: a measure for he amoun of diluion by achromaic ligh Inensiy: is relaed o brighness The marix (A Q) 1 is also used o ransform rsimuli values in he primary sysem P o he risimuli in he sysem Q. RGB-cube wih he Black-Whie axis in uprigh posiion. Top view of RGB-cube. Digial Color Imaging 13 Top view of RGB-cube sreched o: a hexagon, a circle of a riangle. Digial Color Imaging 15 RGB color model RGB model uses he primaries Red, Green and Blue o produce a color. The specral componens of red, green, and blue are added. HSI color model II Noice ha he slices are no percepually uniform. Digial Color Imaging 14 Digial Color Imaging 16
5 HSI Hue Sauraion Inensiy CMY / CMYK color model The CMY color model consiss of he secondary colors of emied ligh. CMY are he primary colors of pigmens. The pigmens absorbs cerain wavelenghs of he illuminaion source and subrac hese wavelenghs from he refleced ligh. Simple conversions such as: C = 1 R, M = 1 G, Y = 1 B, will no produce a faihful reproducion of an RGB encoded color scene by a priner on paper. Addiive primaries Digial Color Imaging 17 The reproducion of a specified color in XYZ by a CMY device requires careful calibraion, resuling in nonlinear ransforms. Subracive primaries Digial Color Imaging 19 HSI Uniform color spaces Percepually uniform color spaces are: CIE L*a*b* (and CIE L*u*v*) Y L = 116h 16 Y W X Y a = 500 h h X W Y W Y Z b = 200 h h Y W Z W L: Lighness a*: from red o green b*: from yellow o blue wih 3 q q> hq ( ) = 7.787q q X W, Y W, and Z W are reference whie risimuli values (D65 illuminaion) The Jus Noiceable Difference (JND) in a*b*-space is 2.3 Digial Color Imaging 18 Digial Color Imaging 20
6 CIE L*a*b* Color copier The aim of a color copier is o produce a color copy ha resembles he color original as good as possible for he sandard human observer. Color recording Can all visible colors be recorded? Wha is he gamu of he recording device? Calibraion of inpu device: RGB in <-> XYZ Color reproducion Can all colors be reproduced? Gamu mapping! Calibraion of oupu device: XYZ <-> CMYK ou ; XYZ <-> RGB ou Digial Color Imaging 21 Digial Color Imaging 23 Overview of Color Models Sysem overview Device-dependen color models depend on he primaries of he hardware. RGB: cameras and scanners employ Red, Green and Blue specral filers. RGB: moniors employ Red, Green and Blue emiing phosphors. CMY / CMYK: priners deposi Cyan, Magena, Yellow and black colored pigmens (colorans). HSI: Hue, Sauraion (chroma) and Inensiy are used o describe color, bu are no direcly relaed o he Human Visual Sysem (HVS). HSI is an alernaive represenaion for RGB and can hence be derived from i. Percepual color models use Color Maching Filers (CMF s) CIE RGB: risimulus values obained by CIE RGB CMF s CIE XYZ: risimulus values obained by CIE XYZ CMF s Percepually uniform color models based on CIE XYZ CIE L*u*v*: Lighness and approximaely uniform uv CIE L*a*b*: Lighness and approximaely uniform ab Digial Color Imaging 22 Digial Color Imaging 24
7 Color recording sysems Pseudo color Aspecs ha are imporan for characerizaion are: Lineariy of he individual color channels (RGB) Space invariance Ligh source Color calibraion convers he measured RGB responses o a percepual color model. Use a es char wih color paches (known XYZ for calibraed ligh source, D65) Measure he RGB responses Deermine he relaion beween RGB and XYZ color models Pseudo color is a common echnique o replace he gray-scale by a color map. The color map does no have o bear a relaion wih a physical inerpreaion of he daa. The color map can be seleced in such a way ha some gray-ones sand ou. Digial Color Imaging 25 Digial Color Imaging 27 Color reproducion sysems Pseudo color for deecion Moniors Addiive process: a r(λ) + b g(λ) + c b(λ) Emission specra of he phosphors Lineariy of irradiance: gamma correcion Inkje priners Subracive process: s(λ) 1 (λ) 2 (λ) 3 (λ) r(λ) 1 (λ) 2 (λ) 3 (λ) forward backward I(λ): specrum of he ligh source: i (λ): ransmission specra of he colorans: i (λ) r(λ): Reflecion specrum of he paper: r(λ) Dihering schemes, half oning Calibraion chars ICC profiles Gamu mapping Digial Color Imaging 26 Digial Color Imaging 28
8 Pseudo color: example Color manipulaions Whie balance and color ones The color of he refleced ligh depends on he specrum of he ligh source. Alhough he HVS is relaively insensiive o variaions in illuminaions, a color image sensor is no. Digial Color Imaging 29 Digial Color Imaging 31 Full color image processing Color manipulaions Full color image processing refers o manipulaion and processing of color images. We disinguish: Color manipulaions: global poin operaions o change he conras, color one, whie balance. Color image processing: local processing of color images o exrac, enhance, or process oherwise o produce a color image as oupu. Color segmenaion: pariioning of a color image ino relevan regions based on similariies of color aribues. Digial Color Imaging 30 Digial Color Imaging 32
9 Color image processing Sharpening: 1-Laplace filer Smoohing and sharpening Which color represenaion o choose Filering all RGB channels vs filering he Lighness componen New colors (new hues) can occur Wha abou he gamu? Suppression of noise in color images Processing of all RGB channels Processing of he Lighness channel Digial Color Imaging 33 Digial Color Imaging 35 Blurring: Gaussian filer Color segmenaion Processing of all RGB channels Color vecors (pixels) ha belong o he same objec should form clouds in color space. Problem: Non-uniform illuminaion yields very elongaed racks in RGB color space. Soluion: Use a color model in which he inensiy can easily be decoupled from he color informaion: Hue + Sauraion CIE a + CIE b Processing of he Lighness channel Digial Color Imaging 34 Digial Color Imaging 36
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