Primary colour
Comparison colour
sRGB input · Bradford CAT to D65 · X swaps colours
Chromatic Adaptation
Bradford diagonal scale: ρc = (D·Yww + 1−D)·ρ · D=1 full · D=0 none
Viewing Conditions
Hunt FLL=1 for ALL surrounds → z = 1 + √n always. This is the key Hunt vs CIECAM97s(avg) difference. n = Yb/100
Shortcuts
X — swap A ↔ B  ·  R — reset  ·  C — copy JSON  ·  1 average · 2 dim · 3 dark · 4 D=1 · 5 D=0
#D3AF37
ΔEHunt
FL= · z=
#4A90D9
CIEDE2000: · ΔE76:

Hunt (1994) Appearance Correlates (FLL=1 all surrounds)

Colour J C94 M94 s94 Q94
Primary
Comparison

J=lightness · C₉₄=chroma · h°=hue angle · M₉₄=colourfulness · s₉₄=saturation · Q₉₄=brightness

Hue Composition H (Quadrature)

Colour H (quadrature) Hue description
Primary
Comparison

H: Red(0)→Yellow(100)→Green(200)→Blue(300)→Red(400) · Fraction = % of adjacent unique hues.

Opponent Chromaticity Plane (a, b)

a = Ra − (12/11)Ga + (1/11)Ba · b = (1/9)(Ra + Ga − 2Ba) · filled = A · ring = B · unique-hue rays R/Y/G/B

Hunt FL Luminance Adaptation Factor

FL = 0.2·k4·(5LA) + 0.1·(1−k4)2·(5LA) · k = 1/(5LA+1) · Vertical line = current LA

Response Compression — Hunt 0.73 vs CIECAM02 0.42

Hunt: 40x0.73/(x0.73+2)+1 (gold) · CIECAM02: 400x0.42/(x0.42+27.13)+0.1 (blue)

ΔE Multi-Metric Comparison

CIE 1931 Chromaticity Diagram

x,y chromaticity · spectral locus · D65 white point · filled = A · ring = B

CIECAM97s Cross-Reference (FLL=0 for avg — same Bradford+HPE)

Colour J C₉₇s M₉₇s s₉₇s Q₉₇s
Primary
Comparison

Average surround difference: Hunt z≈1.45 (FLL=1) vs CIECAM97s z=1.0 (FLL=0) → Hunt gives steeper J gradient for dark colours.

CIECAM02 Cross-Reference (CAT02 + sigmoid 0.42)

Colour J C₀₂ M₀₂ s₀₂ Q₀₂
Primary
Comparison

CIECAM02 uses CAT02 matrix + Michaelis-Menten sigmoid (exponent 0.42, baseline +0.1) and z=1.48+√n (no FLL flag).

CIELAB D65 Reference

Colour L* a* b* C*
Primary
Comparison
Export & Share
Copy or export current colour data. JSON includes Hunt94, CIECAM97s, CIECAM02, and CIELAB correlates. Share URL encodes both colours, D, LA, Yb, Yp, and surround.
Multi-Surround Comparison

Compare ΔEHunt, CIEDE2000, and ΔE₇₆ across average, dim, and dark surrounds for the current colour pair. Also shows J and M94 per surround to visualise how lightness and colourfulness shift.

Surround ΔEHunt CIEDE2000 ΔE₇₆ JA JB M94A M94B
Hunt (1994) Standards Overview
R.W.G. Hunt and the Evolution of the Hunt CAM (1958–1995)

Robert William Gainer Hunt (1923–2012) was one of the most influential colour scientists of the 20th century, working at Kodak Research Laboratories in Harrow, UK. His systematic investigation of colour appearance began in the early 1950s and produced a series of increasingly refined models over four decades.

Year Model Key advance
1958 Hunt (1958) First systematic hue-composition quadrature H; brightness modelling
1982 Hunt (1982) Luminance-level adaptation (FL), non-linear response compression
1987 Hunt (1987) Revised Bradford adaptation, surround factors c/F/Nc/FLL
1991 Hunt (1991) Proximal field Yp, induction effects; full appearance attribute set
1994 Hunt (1994) Consolidated model — this implementation; foundation for CIECAM97s
1995 Hunt (1995) Final book edition; minor calibration updates

Legacy: CIE TC1-34 (chair: Mark Fairchild) used Hunt (1994) as the primary source when developing CIECAM97s. The Bradford matrix, Hunt response function, hue composition H, and appearance correlate definitions were adopted wholesale. The main simplification in CIECAM97s was setting FLL=0 for average surround.

The Critical FLL=1 Difference — Hunt vs CIECAM97s Lightness

The most impactful practical difference between Hunt (1994) and CIECAM97s is the treatment of the FLL flag in the z exponent:

z = 1 + FLL · √n    where n = Yb/Yw

Hunt (1994):   FLL = 1 for ALL surrounds
CIECAM97s:    FLL = 0 for AVERAGE surround, = 1 for dim/dark

For Yb=20%, average surround:
Hunt z  = 1 + 1·√0.2 = 1.447 → J = 100·(A/Aw)^(0.69×1.447) ≈ (A/Aw)^0.998
97s z   = 1 + 0·√0.2 = 1.000 → J = 100·(A/Aw)^(0.69×1.0)  = (A/Aw)^0.69

Hunt gives nearly linear J mapping; CIECAM97s compresses mid-tones. Neither is definitively "correct" — they represent different calibration choices.

Bradford Chromatic Adaptation Transform

Bradford CAT (Lam 1985, refined by Hunt & Pointer 1985) uses a spectrally sharpened cone space. The 3×3 sharpening matrix was derived from corresponding-colour experiments at the University of Bradford. It includes a mild blue-boost row to improve prediction of blues under illuminant change.

Pipeline: XYZ → M_Bradford → von Kries scaling (D) → M_Bradford⁻¹ → XYZ_adapted → M_HPE → LMS for appearance computation.

Proximal Field Yp and Chromatic Induction

Hunt (1991) introduced the proximal field — the region of the visual field immediately surrounding the stimulus, typically a few degrees of visual angle. This is distinct from the broader background (Yb) and the overall surround.

The proximal field contribution modifies the achromatic adaptation through an induction factor, modelling simultaneous contrast. When Yp = Yb, the standard Hunt (1994) formulation is recovered exactly.

Hunt (1994) vs CIECAM97s vs CIECAM02 — Key Differences
  Hunt (1994) CIECAM97s CIECAM02
CAT Bradford Bradford CAT02
Compression Hunt 0.73 Hunt 0.73 Sigmoid 0.42
FLL (avg) 1 0 N/A
z formula 1 + FLL·√n 1 + FLL·√n 1.48 + √n
Baseline +1 +1 +0.1
Status Historical Historical CIE 159:2004
Applications & Legacy
  • CIECAM97s (1998): Entire model directly derives from Hunt (1994). CIE 131:1998.
  • CIECAM02 (2002): Refined successor; replaced Bradford with CAT02 and Hunt power function with Michaelis-Menten sigmoid. CIE 159:2004.
  • ICC v4: Early colour management systems built on Bradford adaptation from Hunt.
  • Textile and automotive: Bradford CAT was standard in metamerism indices.
  • Research: Understanding Hunt (1994) is essential for reading all pre-2002 CIE colour appearance literature.
Mathematical Models and Formulas

Bradford Chromatic Adaptation Transform

MBradford = [[ 0.8951,  0.2664, -0.1614],
             [-0.7502,  1.7135,  0.0367],
             [ 0.0389, -0.0685,  1.0296]]

Von Kries diagonal scaling:
Rc = (D·Yw/Rw + 1−D) · R
Gc = (D·Yw/Gw + 1−D) · G
Bc = (D·Yw/Bw + 1−D) · B

XYZadapted = MBradford−1 · [Rc, Gc, Bc]
References & Citations

Primary Sources — R.W.G. Hunt

  1. Hunt, R.W.G. (1982). A model of colour vision for predicting colour appearance in various viewing conditions. Color Research & Application, 7, 95–112.
  2. Hunt, R.W.G. (1987). A revised colour appearance model for related and unrelated colours. Color Research & Application, 12, 73–83.
  3. Hunt, R.W.G. (1991). Revised colour-appearance model for related and unrelated colours. Color Research & Application, 16, 146–165.
  4. Hunt, R.W.G. (1994). An improved predictor of colourfulness in a model of colour vision. Color Research & Application, 19, 23–26.
  5. Hunt, R.W.G. (1995). The Reproduction of Colour, 5th ed. Kingston-upon-Thames: Fountain Press.
  6. Hunt, R.W.G. (2004). The Reproduction of Colour, 6th ed. Chichester: John Wiley.

CIE Publications

  1. CIE 131:1998. CIE 1997 Interim Colour Appearance Model (Simple Version), CIECAM97s.
  2. CIE 159:2004. A Colour Appearance Model for Colour Management Systems: CIECAM02.
  3. CIE 015:2018. Colorimetry, 4th ed.

Secondary Sources

  1. Fairchild, M.D. (2013). Color Appearance Models, 3rd ed. Chichester: John Wiley.
  2. Li, C. et al. (2002). A revision of CIECAM97s for practical applications. Color Research & Application, 27, 49–59.
  3. Luo, M.R. & Hunt, R.W.G. (1998). The structure of the CIE 1997 colour appearance model. Color Research & Application, 23, 138–146.
  4. Lam, K.M. (1985). Metamerism and Colour Constancy. PhD thesis, University of Bradford.
Batch Analysis — Random Colour Pairs

Generate 200 random sRGB colour pairs, compute Hunt (1994) ΔE and CIEDE2000 for each, and display a histogram of ΔEHunt distribution under the current viewing conditions. Results are shown in the table below (first 50 pairs).

Batch Summary
N= Mean= Median= Min= Max=
ΔEHunt Histogram
Batch Results (first 50)
Colour A Colour B ΔEHunt CIEDE2000 JA JB