castleCSF fitting report

Fitting error

Dataset Fitting error Sensitivity adjustment
castleCSF castleCSF
Average training 3.44 [dB] N/A
modelfest 2.57 [dB] 0.996
hdrvdp_csf 2.35 [dB] 1.308
rovamo1993 2.94 [dB] 1.596
laird2006 5.47 [dB] 0.848
snowden1995 4.14 [dB] 1.068
robson1966 2.49 [dB] 1.021
virsu1979 4.64 [dB] 1.354
virsu1982 3.24 [dB] 0.961
wright1983 2.55 [dB] 0.796
colorfest 2.92 [dB] 0.944
hdr_csf 3.76 [dB] 1.000
kim2013 3.82 [dB] 1.110
five_centres 3.33 [dB] 1.150
lucassen2018 2.86 [dB] 0.567
hdr_csf_disc 3.16 [dB] 1.582
kong2018 3.78 [dB] 1.161
vanderHorst1969_b 3.36 [dB] 0.671
hansen2009 6.43 [dB] 0.872

Model parameters

castleCSF
M_lms2acc = 1.0000 1.0000 0 1.0000 -2.3112 0 -1.0000 -1.0000 50.9875 p.rg.sigma_sust = 16.4325; p.rg.beta_sust = 1.15591; p.rg.ch_sust.S_max = [ 681.434 38.0038 0.480386 ]; p.rg.ch_sust.f_max = 0.0178364; p.rg.ch_sust.bw = 2.42104; p.rg.A_0 = 2816.44; p.rg.f_0 = 0.0711058; p.rg.ecc_drop = 0.0591402; p.rg.ecc_drop_nasal = 2.89615e-05; p.rg.ecc_drop_f = 2.04986e-69; p.rg.ecc_drop_f_nasal = 0.18108; p.yv.sigma_sust = 7.15012; p.yv.beta_sust = 0.969123; p.yv.ch_sust.S_max = [ 166.683 62.8974 0.41193 ]; p.yv.ch_sust.f_max = 0.00425753; p.yv.ch_sust.bw = 2.68197; p.yv.A_0 = 2.82789e+07; p.yv.f_0 = 0.000635093; p.yv.ecc_drop = 0.00356865; p.yv.ecc_drop_nasal = 5.85804e-141; p.yv.ecc_drop_f = 0.00806631; p.yv.ecc_drop_f_nasal = 0.0110662; p.ach.ach_sust.S_max = [ 56.4947 7.54726 0.144532 5.58341e-07 9.66862e+09 ]; p.ach.ach_sust.f_max = [ 1.78119 91.5718 0.256682 ]; p.ach.ach_sust.bw = 0.000213047; p.ach.ach_sust.a = 0.100207; p.ach.ach_sust.A_0 = 157.103; p.ach.ach_sust.f_0 = 0.702338; p.ach.ach_trans.S_max = [ 0.193434 2748.09 ]; p.ach.ach_trans.f_max = 0.000316696; p.ach.ach_trans.bw = 2.6761; p.ach.ach_trans.a = 0.000241177; p.ach.ach_trans.A_0 = 3.81611; p.ach.ach_trans.f_0 = 3.01389; p.ach.sigma_trans = 0.0844836; p.ach.sigma_sust = 10.5795; p.ach.omega_trans_sl = 2.41482; p.ach.omega_trans_c = 4.7036; p.ach.ecc_drop = 0.0239853; p.ach.ecc_drop_nasal = 0.0400662; p.ach.ecc_drop_f = 0.0189038; p.ach.ecc_drop_f_nasal = 0.00813619; Parameters for Ach component: p.ach_sust.S_max = [ 56.4947 7.54726 0.144532 5.58341e-07 9.66862e+09 ]; p.ach_sust.f_max = [ 1.78119 91.5718 0.256682 ]; p.ach_sust.bw = 0.000213047; p.ach_sust.a = 0.100207; p.ach_trans.S_max = [ 0.193434 2748.09 ]; p.ach_trans.f_max = 0.000316696; p.ach_trans.bw = 2.6761; p.ach_trans.a = 0.000241177; p.ach_trans.A_0 = 3.81611; p.ach_trans.f_0 = 3.01389; p.sigma_trans = 0.0844836; p.sigma_sust = 10.5795; p.omega_trans_sl = 2.41482; p.omega_trans_c = 4.7036; p.ecc_drop = 0.0239853; p.ecc_drop_nasal = 0.0400662; p.ecc_drop_f = 0.0189038; p.ecc_drop_f_nasal = 0.00813619; Parameters for RG component: p.ch_sust.S_max = [ 681.434 38.0038 0.480386 ]; p.ch_sust.f_max = 0.0178364; p.ch_sust.bw = 2.42104; p.A_0 = 2816.44; p.f_0 = 0.0711058; p.sigma_sust = 16.4325; p.beta_sust = 1.15591; p.ecc_drop = 0.0591402; p.ecc_drop_nasal = 2.89615e-05; p.ecc_drop_f = 2.04986e-69; p.ecc_drop_f_nasal = 0.18108; Parameters for YV component: p.ch_sust.S_max = [ 166.683 62.8974 0.41193 ]; p.ch_sust.f_max = 0.00425753; p.ch_sust.bw = 2.68197; p.A_0 = 2.82789e+07; p.f_0 = 0.000635093; p.sigma_sust = 7.15012; p.beta_sust = 0.969123; p.ecc_drop = 0.00356865; p.ecc_drop_nasal = 5.85804e-141; p.ecc_drop_f = 0.00806631; p.ecc_drop_f_nasal = 0.0110662;

↸CSF model: castleCSF

L+M
L-M
S-(L+M)

Color mechanisms
Sustained and transient response
Peak sensitivity

Legend

To keep the plots legible, only up to 3 models are plotted.

↸Dataset: [modelfest] ModelFest

Achromatic CSF as a function of frequency

↸Dataset: [hdrvdp_csf] HDR-VDP CSF

Achromatic CSF as a function of frequency

Achromatic CSF as a function of size

↸Dataset: [rovamo1993] Rovamo et al. 1993

CSF as the funcation of stimulus area

CSF as the function of spatial frequency

↸Dataset: [laird2006] Laird et al. 2006

Achromatic CSF as a function of temporal frequency for different spatial frequencies

↸Dataset: [snowden1995] Snowden et al. 1995

Temporal contrast sensitivity at different spatial frequencies and luminance levels

↸Dataset: [robson1966] Robson 1966

Spatial CSF for different temporal frequencies

Temporal CSF for different spatial frequencies

↸Dataset: [virsu1979] Virsu & Rovamo 1979

Contrast sensitivity of central and peripheral vision as a function of spatial frequency and eccentricity

↸Dataset: [virsu1982] Virsu et al. 1982

Contrast sensitivity as the function of frequency

↸Dataset: [wright1983] Wright and Johnson 1983

CSF as function of eccentricity

↸Dataset: [colorfest] ColorFest

Chromatic CSF as a function of frequency

↸Dataset: [hdr_csf] High Dynamic Range CSF

CSF as the function of frequency at different luminance levels (fixed number of cycles)

CSF for different number of cycles

↸Dataset: [kim2013] Chromatic CSF [Kim et al. 2013]

Chromatic CSF as a function of frequency

↸Dataset: [five_centres] Five centres [Xu et al. 2020]

Chromatic CSF as ellipses

Chromatic CSF as a function of frequency

↸Dataset: [lucassen2018] Lucassen et al. 2018

Lucassen2018 CSF sensitivity vs spatial frequency

↸Dataset: [hdr_csf_disc] High Dynamic Range Disc CSF

CSF as the function of size at different luminance levels

↸Dataset: [kong2018] Kong et al. 2018

Chromatic CSF as a function of temporal frequency (9 background colors and 4 color directions in uv space

↸Dataset: [vanderHorst1969_b] Van der Horst & Bouman 1969

Static chromatic contrast thresholds as a function of spatial frequency

Static chromatic contrast thresholds as function of luminance

Chromatic contrast thresholds for travelling sine waves as function of spatial frequency

Chromatic contrast thresholds for travelling sine waves as function of temporal frequency

↸Dataset: [hansen2009] Hansen et al. 2009

CSF as the function of eccentricity