Three main applications of Colorimetry The CIECAM02 and Its Newly Colour specification Derived Uniform Colour Spaces -XYZ Colour difference evaluation - CIELAB, CMC, CIE94, CIEDE2000 Ronnier Luo Colour appearance prediction - CIECAM97s, CIECAM02 Colour and Imaging Group University of Leeds Can a unique colour model to perform all tasks? Cross-Media Colour Reproduction Colour Management Systems RGB RGB RGB CMYK RGB CIECAM RGBRGB RGB LCH RGB RGB ConsortiumRGB CIECAM02 Aims Input and output parameters n To develop uniform colour spaces based Brightness (Q) upon CIECAM02, the newly adopted CIE Lightness (J) colour appearance model. Redness-Greenness (a) X Y Z Yellowness-blueness (b) o To fit the experimental data sets based CIECAM02 Colourfulness (M) Chroma (C) upon large and small colour differences. Saturation (s) Hue angle (h) Hue composition (H) X Y Z L Surround: Average, w w w A Yb 8/1/2005 Dim and Dark 1 X Y Z Chromatic adaptation Xo Yo Zo CIECAM02 Hue Luminance An area appears to be similar to one of the 4 100 Y Cone responses R, G, B unitary hues: red, yellow, G50Y Y50R green and blue,or a J, C, h; Q, M, h ? combination of two of R’ , G’ , B’ Dynamic responses a a a them. Uniform colour space Hue composition (H)- 100 G colour appearance 100 R Opponent Achromatic A (Y50R) a, b Process Signal J, C, H; Q, M, H? J, Q Appearance Hue angle (h)- Colour appearance space H, h attributes Colour difference (0o to B50G R50B o s, C, M 360 ). 100 B Background, media/surround Different structures for calculating ∆E Measure of Fit – PF/3 2 2 2 ∆EJCh = ∆J + ∆aCh + ∆bCh 2 2 2 ∆EJMh = ∆J + ∆aMh + ∆bMh 2 2 2 PF / 3 = 100(γ + CV /100 +VAB −1) / 3 ∆EJsh = ∆J + ∆ash + ∆bsh where CV : coefficient of variation aCh = C cos(h) and bCh = C sin(h) γ : gamma VAB : derived by Schultze aMh = M cos(h) and bCh = M sin(h) aCh = s cos(h) and bCh = ssin(h) For a perfect agreement, CV=0, γ =1 and VAB =0 Which attributes to use (in PF/3 units)? A simple modification on M and J Dataset No. of ∆EJCh ∆EJMh ∆EJsh pairs ∆E'= ∆J '2 +∆a'2 +∆b'2 144 30 64 Zhu 29 where OSA 128 22 21 37 GUAN 292 27 24 44 ∆J '= J '1 −J '2 , ∆a'= a'1 −a'2 , ∆b'= b'1 −b'2 BFD-Badu 238 31 29 43 and Pointer 1038 36 35 55 Munsell 844 31 28 48 M '1 = k1 × ln(1+ k2 M 1 ), M '2 = k1 × ln(1+ k2 M 2 ), LCD Average 2684 28 27 49 a1 '= M '1 cos(h1 ), b1 '= M '1 sin(h1 ), SCD 3652 49 47 78 a2 '= M '2 cos(h2 ), b2 '= M '2 sin(h2 ) 1.7J1 1.7J 2 LCD (Large colour-difference): mean of 10.0 ∆E*ab units J '1 = , J '2 = 1+ 0.007J1 1+ 0.007J 2 SCD (Small colour-difference): mean of 2.5 ∆E*ab units 2 Performance for fitting LCD data Performance for fitting SCD data Colour spaces PF/3 Model Type PF/3 CIELAB 26 CIELAB UCS 52 NC_IIIC 27 IPT UCS 53 CIECAM02 UCS 47 IPT 26 DIN99d UCS 35 GLAB 24 CMC CDE 38 OSA 24 CIE94 CDE 37 CIECAM02 27 CAM02-SCD UCS 34 CAM02-LCD 22 CIEDE2000 CDE 33 UCS: Uniform colour space; CDE: Colour difference formula Comparing between CA, LCD and SCD data OSA samples 100 100 60 4 60 50 50 3 80 80 40 40 30 2 30 60 60 20 20 1 10 10 J' 0 40 0 0 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 40 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 -4 -3 -2 -1 0 1 2 3 4 -10 -10 -1 M-LCD & M-SCD & M-SCD M-LCD -20 -20 20 20 -30 -30 -2 -40 -40 0 -3 0 -50 0 20 40 60 80 100 -50 020406080100 -60 -60 -4 J a M-CIECAM02 a* P CIELAB IPT CAM02-LCD Consistent improvement of 2% M-LCD: dash line; M-SCD: solid line from J to J’ 120 50 120 100 b* b b* b 40 100 100 80 80 30 80 60 60 20 60 40 10 40 40 a 20 0 20 20 a 0 a* -10 a* 0 0 -20 -20 -20 -20 -30 -40 -40 -40 -40 -60 -40-30-20-100 1020304050 -60 -60 -60 -40 -20 0 20 40 60 80 100 120 -60 -40 -20 0 20 40 60 80 100 120 -60 -40 -20 0 20 40 60 80 100 CIELAB DIN99d CIELAB CAM02-SCD 3 50 100 b b BFA - Data under Illuminant A 40 80 30 60 ①Collected at University of Bradford 20 40 ② 10 under illuminant A 20 a 0 ③Ratio method + grey scale method a 0 -10 ④1053 pairs of surface colours -20 -20 ⑤Around 51 colour centres -30 -40 * -40 -60 ⑥Average ∆ E : 2.9 -40 -30 -20 -10 0 10 20 30 40 50 ab -60 -40 -20 0 20 40 60 80 100 DIN99d CAM02-SCD BFDA – CDF for illuminant A Performance of Colour Difference Formulae nIntroduced by Luo and Rigg in 1987 60 nDeveloped from BFA 52 2 ' 2 ' 2 ∆E(BFDA(l : c)) = ()∆L(BFD) / l + (∆C / cDC ) + (∆H / DH ) 50 where 40 37 36 ' 35 35 35 34 DC = 0.021C /(1− 0.042C) + 0.866 ' ' ' ' 30 DH = DC (G TA +1− G ) 25 PF/3 0 0 0 T A= 0.671− 0.040cos(h − 296 ) − 0.213cos(2h −101 ) + 0.82cos(3h − 57 ) 20 + 0.056cos(4h +1290 ) − 0.034cos(5h + 2020 ) 4 4 10 G' = C /(C +1900) L(BFD) = 54.6log(Y +1.5) − 9.6 0 B C 4 0 D D d A 2 2 A M E9 00 F C 99 D C = a + b* and a =1.67a* EL C I 2 B L IN BF I C DE D C IE h = arctan(b*/ a) C Performance to fit an Illuminant A data set CIELAB vs. CAM02-SCD Model PF/3 CIELAB 52 CIECAM02 43 CAM02-LCD 37 CIEDE2000 35 DIN99d 34 CAM02-SCD 32 BFDA 25 Mean A/B = 2.38 Mean A/B = 1.64 A/B = Semi-major/semi-minor axes 4 Conclusions ¾ Simple modifications were made to CIECAM02 by fitting the LCD and SCD data sets. ¾ Two versions, CAM02-LCD and CAM02-SCD were developed for evaluating LCD and SCD data sets respectively. ¾ The BFD Illuminant A data set was also tested showing the superiority of CAM02-SCD. ¾ CAM based colour difference formula has a major advantage: ¾ Taking into account the change of viewing conditions such as illuminant, luminance, background, surround, etc 5.