THE NON-ADDITIVITY PHENOMENON IN MESOPIC PHOTOMETRY
Z. VAS1*, P. BODROGI2, J. SCHANDA1, G. VARADY3
Svetotechnika 3/2010. 17-24.
1: Virtual Environment and Imaging Technologies Laboratory,
University of Pannonia, Egyetem u. 10, H-8200 Veszprém, Hungary
2: Laboratory of Lighting Technology, Technische Universität Darmstadt,
Hochschulstraße 4a, D-64289 Darmstadt, Germany
3: University of Pécs, Pollack Mihály Faculty of Engineering,
Dept. of System- and Software Tech., Boszorkány u. 2, H-7624 Pécs, Hungary
*corresponding author: mailto:vas.zoltaan@gmail.com
ABSTRACT
At mesopic luminance levels, both the rods and the cones are active. Typical applications of
the models of mesopic vision include street lighting, car headlamp lighting, emergency
lighting and lighting for security purposes. Present day standards are based on the photopic
spectral luminous efficiency function V(λ) which describes the luminous spectral responsivity
of cone vision under photopic lighting conditions. Under mesopic conditions, differences
between measured or calculated values and visually observed ones occur. Current mesopic
models use the combination of the V() and V’() functions to predict mesopic visibility.
Visual experiments were carried out to explore human mesopic visual detection mechanisms.
These experiments showed spectral non-additivity of monochromatic components of the
observed light according to the activity of the chromatic channels. In the present paper, these
experimental data were analyzed by a recently published model, the so-called CHC2 model
[1] (from the abbreviation of chromatic conspicuity). The CHC2 model predicts the spectral
non-additivity of mesopic detection performance and enables an accurate determination of
threshold detection sensitivity for any spectral power distribution. For certain object
chromaticities, detection sensitivity is lower than predicted by current mesopic models. This
is of major importance in street lighting as the current models may underestimate the
detection threshold by several tens of percents. The CHC2 model can be a step forward to an
advanced (chromatic) detection based model of mesopic photometry.
Keywords: mesopic luminance, detection threshold, non-additivity, CHC2 model
INTRODUCTION
Several models of mesopic vision were developed in the past. They can be grouped into two
main groups: one for brightness evaluation [2] and the other one for modelling visual
performance, mainly reaction time [3,4]. While it is well known that brightness perception
does not follow Abney’s law, not even in the photopic range, this effect was not investigated
for object detection performance under mesopic conditions. In real life situations, the two
most important tasks are the speed of human response and object detection. In this paper, the
latter group, specifically mesopic threshold detection performance will be discussed in detail
and the important question of spectral non-additivity will be dealt with.
Mesopic brightness models are based on heterochromatic brightness matching
comparing the perceived brightness of the test field with the brightness of a reference field.
Recently, it was shown [2, 3, 4] that mesopic perceived brightness and visual performance
(detection, recognition, reaction time) have different spectral responsivity because the tasks of
the observers are different. Significant differences were found because different tasks activate
different retinal and post-retinal processing mechanisms. Two types of visual performance
modelling are known: achromatic modelling [5] (resembling the luminance perception which
is spectrally additive) and chromatic modelling (which provides chromatic information and
can be formulated to take non-additivity into account) [4]. An achromatic model is e.g. the so
called X-model which uses a linear combination of V() and V’() to predict the mesopic
spectral luminous efficiency function: Vm()=xV()+(1-x)V’() where x ranges between 0 and
1 (see the Appendix). A similar model has been proposed by CIE TC 1-58 6.These models
predict “bell-shaped” responsivity curves (similar to the photopic or scotopic spectral
sensitivity curve) which shift between V() and V’() depending on the x parameter. The
MOVE research consortium proposed a chromatic model as well. This predicts a “multi-peak
behaviour” of the mesopic spectral responsivity function for object detection and recognition.
„Multi-peak” behaviour means the fact that the luminous efficiency function in the mesopic
range has more local maxima in the visible spectrum instead of forming a “bell-shaped”
curve. This behaviour was found to be stronger for intermediate (or higher) mesopic
luminance levels and for increasing target sizes. This effect was associated with perceiving
hues at the detection threshold showing the activity of chromatic mechanisms.
Towards the end of the 90’s of the last Century and at the beginning of the XXIst
century, the interest in the performance based approach in developing mesopic photometry
grew [5-8] because in real-life situations the detection and recognition of visual objects is
more relevant for safety than the visual assessment of brightness.
Until the mid fifties of the last Century, high pressure mercury lamps were used in
street lighting followed by high pressure sodium (HPS) lamps. At present, a tendency can be
observed to change to so-called “daylight” metal halide (MH) lamps with more bluish light.
Based on the achromatic mesopic models (e.g. 3), metal halide lamp light should provide
better visibility under mesopic conditions. Recently, LEDs have been introduced in street
lighting applications. As the spectral power distribution (SPD) of the LEDs can be easily
modified, one could optimize it to obtain better visual performance and energy saving if the
predictions of these mesopic models could be used. Experiments also showed that under
mesopic conditions visual acuity of young observers was better under cool white LED light
sources than under warm white LEDs. For elderly observers (above 65 years of age), no such
better visual performance could be obtained [9]. Thus it is critical which spectral responsivity
curve is used to set optimal SPDs for street lighting. Unfortunately, industry still misses the
guidance for this most advantageous SPD. The present visual experiments and their
interpretation using the CHC2 model were intended to help develop guidelines to achieve
optimum spectral power distributions for different mesopic lighting applications.
EXPERIMENTAL METHOD
The experimental layout can be seen in Figure 1.a. The task lights were projected on a large
achromatic background field illuminated at 0.5 cd/m2 by a lamp built from white phosphor
LEDs (chromaticity coordinates are x=0.311; y= 0.330, CCT=6000K). LEDs were chosen
because nowadays LED car headlamps are getting more popular and get also used in street
lighting.
Figure 1.a: Experimental layout. Figure 1.b: SPD of the LED used to illuminate the background screen.
Figure 1.b shows the SPD of the phosphor coated blue emitting LEDs (cool white) used to
illuminate the canvas producing the background field. The size of this canvas was 80° x 60°
to ensure a homogenous achromatic adaptation field. The canvas was installed in a dark room
which ensured the night scenario. Two computer-controlled projectors of the same kind were
used during the experiments to project the visual targets on the canvas. A primary visual
target was a 2° filled disk presented at 20° or at 10° off-axis. It was a light increment
projected onto the background. In the experiments, interference filters of 20 nm half
bandwidth were used (440 nm, 490 nm, 540 nm, 570 nm, 600 nm, 620 nm and their additive
mixtures). The observers were asked to fixate on the centre. Supporting this, a secondary
visual target (a red 2° control target at L=2.5 cdm-2) was used. It was (randomly) either a
number or a pictogram projected onto the centre of the visual field. The observer had to
answer what the control target was. All physical measurements were carried out by a Photo
Research PR 705 spectro-radiometer.
Three major methods (A, B and C) were used in the contrast threshold detection experiments.
In all three methods, the type of the introductory phase was the same: the visual target
emerged from the background with the flicker frequency of 1.67 Hz. According to Padmos
and Norren, the influence of the chromatic mechanisms appears only over a 300 ms flickering
period [11]. The DAC value (digital driving value of the projector) of the primary target was
increased from zero until the observer signalled detection.
Method “A” was built up from two main stages. The first one, as mentioned before, was an
introductory phase to assess a DAC value which was the centre of a ±30 DAC interval
containing 300 randomly generated DAC values to be used in the second phase. These 300
randomly generated stimuli contained 52 stimuli with zero DAC value (i.e. no signal), to be
able to calculate a percent correct point. Stimuli were presented for 2 s and hidden for another
2 s. Observers had to signal the detection of the target. This method was called the method of
constant stimuli.
Method “B” was a “forced choice fixed step size staircase method” [12]. The observer
determined the preliminary DAC value where the stimulus was first perceived during the
introductory phase. This was followed by either increasing the DAC value or decreasing it.
The DAC value was decreased (increased) by 1 DAC unit until the observer nearly couldn’t
(could) detect the target. This resulted in another direction change. After these changes were
done several times after each other, these values were evaluated (mean value from the least 15
DAC values) to get the final threshold value.
A new method was formulated as type “C” to get an even better estimation of the threshold
value. The observation started with the introductory phase to assess a rough DAC detection
threshold value. There were numerous primary visual targets presented after each other with
the same DAC value. These targets (where the DAC was the same) formed a group. Upon the
given answers to the group members, a step up (increasing the DAC value) or down
(decreasing the DAC value) was calculated. Step sizes are shown in Table 1. This method
converges faster and more precisely to the threshold value than the other ones. If we get closer
to the real threshold, the hit ratio gets lower. This uncertainty can be avoided by the help of
multiple steps and target groups. The observer doesn’t know that there are targets presented
with the same DAC value (group) and this leads to different detection probabilities for the
same DAC values. This way, smaller or bigger steps can be performed and a better asymptotic
convergence can be reached. This property is very important in mesopic investigations
because the observer tends to get tired and signals a higher threshold DAC value than the real
one.
Table 1. Performed steps based on the hit ratio.
Hit ratio
Performed steps
100%
75%
-3 DAC (decreasing)
-1 DAC (decreasing)
Must repeat, with the same
DAC value
1 DAC (increasing)
3 DAC (increasing)
50%
25%
0%
In method “C” the evaluation was the following: a mean value was calculated from the last 10
DAC values (increase/decrease direction change) if an approximately 50% hit ratio was
observed twice. In all three methods, if the observer missed the control target then that given
answer was discarded as it indicated that the fixation of the observer shifted from the fixation
point.
In the 20° experiments, 4 female and 1 male, while in the 10° experiments 1 female and 1
male (aged between 20 and 26) observer took part. For each target chromaticity, they repeated
the 20° tests 15 times and the 10° experiments 18 times, all of them had good colour vision
tested by the FM100 Hue Test.
Tests were carried out not only with single narrow band stimuli but also with composite
stimuli to explore the effect of spectral non-additivity, see Table 2.
Table 2. Interference filter sets used to investigate the non-additivity effect in the mesopic range.
Interference filter sets used in the non-additivity experiments
440 nm
540 nm
490 nm
620 nm
620 nm
600 nm
440 nm + 620 nm
540 nm + 620 nm
490 nm + 600 nm
Detection threshold was assessed both by superposing the two narrow band radiations i.e. by
projecting their additive mixture (e.g. 490 nm plus 600 nm) as an increment on the
background and by showing them individually (e.g. either 490 nm or 600 nm).
COMPUTATIONAL METHOD
If Abney’s law of additivity was fulfilled in the mesopic object detection task then the
luminance sum of the two quasi-monochromatic (e.g. 490 nm, and 600 nm) lights filtered by
the interference filters separately could be used to predict the detection probability of the
mixture of the two (490 nm+600 nm). From the luminance increment values, a usable contrast
threshold could be calculated. This contrast threshold value is the minimal luminance
difference between the background and the target (i.e. the increment on the background)
needed to detect the visual target. Unfortunately, due to the activity of chromatic mechanisms,
Abney’s law cannot be applied and, instead of applying the concept of mesopic luminance, a
more sophisticated (chromatic or spectrally opponent) model of mesopic detection is needed
like the recently developed CHC2 model[1. The CHC2 model takes the chromatic
mechanisms of the visual system into account to yield a more accurate predictor of detection
performance.
The CHC2 model[1] represents a novel approach to predict the detectability of mesopic light
increments at or around the detection threshold. The contrast metric CCHC2 is computed from
the CL, CM, CS and CR cone and rod contrasts where CR rod contrast is calculated using V’(λ)
rod spectral sensitivity –because only the rods contribute to scotopic vision – and the L(),
M() and S() are the cones spectral sensitivity functions published by Stockman and Sharpe
[13] are used to determine CL, CM, CS and CR. The contrasts are calculated as the ratio of the
target increment SPDs and the background SPDs. As an example, in Figure 2a, both the
background SPD and the increments are shown and in Figure 2b, only the increment is shown.
SPD of the background and the increments
1,4E-05
1,0E-06
1,2E-05
SPD of the
increment
SPD of the
background
8,0E-07
Rel. Units
Rel.Units
1,0E-05
SPD of the increments
1,2E-06
SPD of the
increment
8,0E-06
6,0E-06
6,0E-07
4,0E-07
4,0E-06
2,0E-07
2,0E-06
0,0E+00
0,0E+00
350
400
450
500
550
600
Wavelength(nm)
650
700
750
350
400
450
500
550
600
Wavelength(nm)
650
700
750
Figure 2a. SPD of the background and the SPD added to the background by the increment (at λ=490 nm and
λ=600 nm). Figure 2b. SPD of the increment generated by using two interference filters (λ=490 nm and λ=600
nm) in two parallel optical channels.
The spectral difference between the two curves (Figure 2a) shows the threshold contrast
needed for the detection of the object (i.e. the visual target).
The CL, CM, CS and CR values are calculated from the L, M, S and R cone and rod sensitivities,
weighted by the SPDs of the increment (shown in Figure 2b) –resulting ΔL, ΔM, ΔS and ΔR-,
and the background(shown in Figure 2a, dashed line) –resulting Lb, Mb, Sb, Rb values-. This
process is done in the following way[1]:
780
L 
 L (  )  (  ) d (  ) .
(1)
380
780
Lb 
 L ( ) 
b
( ) d ( ) .
(2)
380
CL  L / Lb .
(3)
CM, CS and CR are calculated in the same way. () is the absolute SPD of the light
increment i.e. it can be both quasi-monochromatic or spectrally composite, χb(λ) is the
absolute SPD of the background. The CCHC2 metric uses receptor-specific contrast adaptation
[1] which is realized through the expressions of CL, CM, CS and CR. Furthermore,
YMPb ( ) 
780
V
MP
380
( )  b ( ) d ( ) ,
(4)
is a “mesopic luminance like” quantity for the background SPD where VMP(λ) is a multi-peak
spectral responsivity curve similar to Kurtenbach et. al’s [14] for the mesopic luminance
range.
VMP ( )  L L( )  M M ( )  SS ( )  RV '( )   L L( )   M M ( )
(5)
where the {αL, αM, αS, αR, γL, γM} parameter set represents the relative rod-cone contributions
as well as the contribution of the L-M opponency. The parameter set is the result of
optimization to the present, psycho-physically measured mesopic detection threshold data at
10° and 20° eccentricity derived from pooling the results of methods “A”, “B” and “C” and
requiring that the standard deviation should be minimal among the parameters.
With the help of Equations (1)-(5) the CCHC2 contrast metric is defined in the following way
[1]:
CCHC2= L (Lb /YMPb )CL + M (M b /YMPb )CM +S (S b /YMPb )CS
+ R (Rb /YMPb )CR +| L (Lb /YMPb )CL - M (M b /YMPb )CM|
(6)
where the {αL, αM, αS, αR, γL, γM} is the same as for VMP(λ). In CHC2[1], the relation between
αL and αM was set to αL=1.55αM according to Sharpe and Stockman[13]. Recently, this ratio
was changed to αL=1.89αM according to Sharpe and Stockman[15].
To compare the results of the new CHC2 contrast metric, three achromatic models
from literature were chosen. These three recent mesopic photometry metrics are the MOVE
model4, the so- called X-model 3 and CIE TC 1-58’s mesopic model 6. The three models
are very similar to each other and differ considerably from the CHC2 metric[1]. They are
achromatic models that exhibit spectral additivity if
1. the SPD of the background is fixed,
2. the background is large enough and
3. the target is not too intensive compared to the background.
Latter three assumptions are necessary to ascertain a fixed state of adaptation independent of
the intensity and spectral content of the visual target appearing on the background.
To compare the performance of the CHC2 contrast metric, the CMOVE, CX and CTC1-58 contrast
values were calculated in the following way:
C
Ltarget  Lbackground
Lbackground
,
(7)
where Ltarget and Lbackground are the luminance values of the increment and the background, both
calculated with the appropriate model (X, MOVE, CIE TC1-58).
RESULTS
Table 3 shows the optimized {αL, αM, αS, αR, γL, γM} parameter sets for 10° and 20°
eccentricities and with their standard deviations which represent the inter-observer variability
of 2 or 5 observers.
Table 3. Optimized {αL, αM, αS, αR, γL, γM} parameter sets of the CHC2 model for 10° and 20° eccentricity with
standard deviations (SD). The values are based on the present experimental dataset.
Viewing angle /
Parameters and SDs
αL
SD
αM
SD
αS
SD
αR
SD
γL
SD
γM
SD
10°
0.76
0.21
0.40
0.09
0.71
0.23
0.31
0.19
0.94
0.22
0.64
0.21
20°
0.68
0.15
0.36
0.05
0.46
0.26
0.69
0.11
1.02
0.32
1.32
0.21
The {αL, αM, αS, αR, γL, γM} datasets shown in Table 3 are used in Equation (6) to calculate the
minimal contrast needed for safe detection between background and increment SPDs.
Figure 3. Normalized spectral sensitivity curves for 10° and 20° eccentricity are plotted with standard
deviation representing the inter-observer variability of the CHC2 contrast metric. The normalized spectral
detection sensitivity curves by the MOVE, X and CIE TC1-58 are also shown for 0.5 cd/m2 background
luminance and x=0.311; y= 0.330 background chromaticity
The activity of the chromatic mechanisms can be illustrated by plotting the spectral detection
responsivity curves according to the CHC2 model, and as a comparison, also according to the
MOVE, the X and the CIE TC1-58 models. Figure 3 shows this comparison for the 6000 K
background (the “x” parameters for the X-model and the MOVE model, and the “m”
parameter were recalculated for 0.5 cd/m2: 0.768, 0,578, 0.690 respectively). All achromatic
curves are different from the CHC2 model: they have only one maximum. CHC2 shows two
or more local maxima, depending on eccentricity. This property is caused by the influence of
the chromatic mechanisms that are spectrally opponent, and by the spatial distribution of the
rods and cones on the human retina. The data shown in Table 4 are calculated from the
averages of the observed and physically measured values with the help of methods “A”, “B”
and “C”. More than 600 observations for 20° eccentricity (5 observers) and nearly 350 for 10°
off-axis (2 observers) were done.
Table 4. Mean contrast values calculated with the help of Equation (6) with minimum and maximum values
of the 95% confidence intervals shown in brackets for 10° and 20° eccentricity. The column SD/M lists the
normalized standard deviation of the mean contrast values, shown in the Table (M i.e. the mean of the mean
values is the normalizing factor) [16].
Contrasts /
Wavelength
(nm)
CCHC2 10°
CCHC2 20°
440
620
440+620
540
620
540+620
490
600
490+600
570
1.35
(1,1.71)
0.92
(0.57,1.29)
0.91
(0.56,1.27)
1.01
(0.68,1.33)
1.76
(1.2,2.32)
1.53
(1.08,2)
1.27
(0.75,1.79)
1.22
(0.81,1.65)
0.94
(0.61,1.26)
1.01
(0.68,1.33)
1.48
(0.95,2.02)
0.67
(0.29,1.11)
0.77
(0.32,1.22)
1.36
(0.97,1.75)
1.04
(0.65,1.43)
0.91
(0.52,1.3)
1.23
(0.87,1.59)
1.26
(0.57,1.62)
1,70
(1.34,2.06)
1.22
(0.87,1.59)
SD /
M
0.24
0.27
Table 5 shows the calculated contrast values –with the help of Equation (6) and Equation (7)for the four models at 10° eccentricity at the given central wavelength with standard
deviations, representing inter-observer variability of 2 observers.
Table 5. Mean contrast values calculated with the help of Equation (6) and Equation (7) with minimum and
maximum values shown in brackets (95% confidence intervals) for 10° eccentricity. The column SD/M lists
the normalized standard deviation of the mean contrast values shown in the Table (M is the normalizing
factor).
Contrast
metric/
Wavelength
(nm)
CMOVE
CX
CTC1-58
CCHC2
440
620
440+620
540
620
540+620
490
600
490+600
570
0.80
(0.44,1.15)
0.59
(0.23,0.94)
0.66
(0.3,1.02)
1.35
(1,1.71)
1.04
(0.69,1.4)
1.39
(1.03,1.74)
1.25
(0.9,1.61)
0.91
(0.56,1.27)
1.58
(1.02,2.14)
1.71
(1.15,2.27)
1.65
(1.09,2.21)
1.76
(1.2,2.32)
2.43
(1.91,2.95)
2.49
(1.97,3.01)
2.47
(1.95,2.99)
1.27
(0.75,1.79)
1.19
(0.86,1.51)
1.52
(1.19,1.84)
1.39
(1.06,1.71)
0.94
(0.61,1.26)
2.38
(1.84,2.91)
2.70
(2.17,3.24)
2.58
(2.04,3.11)
1.48
(0.95,2.02)
1.71
(1.26,2.16)
1.21
(0.76,1.66)
1.40
(0.95,1.85)
0.77
(0.32,1.22)
1.27
(0.88,1.66)
1.58
(1.19,1.97)
1.46
(1.07,1.85)
1.04
(0.65,1.43)
2.24
(1.88,2.6)
1.97
(1.61,2.33)
2.07
(1.71,2.43)
1.23
(0.87,1.59)
2.65
(2.29,3.01)
3.10
(2.74,3.45)
2.92
(2.57,3.28)
1.70
(1.34,2.06)
SD /
M
0.35
0.38
0.39
0.24
A true contrast metric should yield the same contrast values at the detection threshold for
different targets of different chromaticities. The data in Table 5 show that the standard
deviation of the detection contrast values for CCHC2 is less than the standard deviation of the
other metrics. This means that CCHC2 can be considered as a good contrast metric which can
be used to predict contrasts for different object chromaticities and different background SPDs.
Table 6. Mean contrast values calculated with the help of Equation (6) and Equation (7) with minimum and
maximum values shown in brackets (95% confidence intervals) for 20° eccentricity. The column SD/M lists
the normalized standard deviation of the mean contrast values shown in the Table (M is the normalizing
factor).
Contrast
metric/
Wavelengt
h (nm)
CMOVE
CX
CTC1-58
CCHC2
440
620
440+620
540
620
540+620
490
600
490+600
570
0.57
(0.31,0.94)
0.32
(0.2,0.55)
0.41
(0.28,0.69)
0.92
(0.57,1.29)
1.03
(0.67,1.38)
1.29
(0.93,1.64)
1.19
(0.83,1.54)
1.01
(0.68,1.33)
1.48
(0.92,2.04)
1.39
(0.83,1.95)
1.41
(0.85,1.97)
1.53
(1.08,2)
2.96
(2.44,3.48)
3.01
(2.49,3.54)
3.00
(2.48,3.52)
1.22
(0.81,1.65)
1.03
(0.67,1.38)
1.29
(0.93,1.64)
1.19
(0.83,1.54)
1.01
(0.68,1.33)
2.93
(2.39,3.46)
3.11
(2.57,3.65)
3.04
(2.51,3.58)
0.67
(0.29,1.11)
1.60
(1.15,2.05)
1.10
(0.65,1.55)
1.29
(0.84,1.74)
1.36
(0.97,1.75)
0.82
(0.43,1.21)
1.04
(0.66,1.43)
0.96
(0.57,1.35)
0.91
(0.52,1.3)
1.82
(1.46,2.18)
1.75
(1.39,2.1)
1.77
(1.41,2.13)
1.26
(0.57,1.62)
2.75
(2.39,3.1)
3.24
(2.89,3,6)
3.05
(2.7,3.38)
1.22
(0.87,1.59)
SD /
M
0.47
0.57
0.52
0.27
Table 6 shows the calculated contrast values –with the help of Equation (6) and Equation (7)for the four models at 20° eccentricity at the given central wavelengths together with their
standard deviations representing inter-observer variability of 5 observers. The normalized
standard deviation is considerable for every model but it is significantly less for the case of
CHC2. This means that the CHC2 contrast metric is more appropriate for 20°eccentricity than
the achromatic models. Note that the mean values of the measured data for 620 nm were used
twice. This is the reason why the values are the same for the second and the fifth columns.
DISCUSSION
The spectral sensitivity curves predicted by the CHC2[1] model (Figure 3) show more local
maxima depending on viewing angle. They have a different shape than the curves of the other
(achromatic) models from literature. This difference is caused by including the chromatic
channels. These “multi-peaked” curves let us conclude that there are multiple mesopic
detection mechanisms working on the human retina.
As can be seen from Tables 5 and 6, the achromatic models –MOVE, X and CIE TC1-58predict the detection contrast threshold values with a larger standard deviation than the CHC2
contrast metric. An ideal contrast metric model should yield the same contrast value at the
detection level for different chromaticities. According to these facts, the CHC2 model can
give better predictions than previous (both chromatic models and achromatic models using
spectral integration) because the CHC2 model describes chromatic influence in a more
plausible (i.e. non-additive) way. Hence, by including this chromatic opponency, spectral
non-additivity in the mesopic range can be accounted for.
Also note that the above achromatic models are based on visual experiments at 10°
eccentricity only. At 20° off-axis, they can be compared only with care with CHC2 which is
the result of both 10° and 20° visual tests. The achromatic models, MOVE, TC1-58 and X do
not take the influence of eccentricity dependence into account, only Lp and Ls are considered
to be important. This fact may also lead to errors in the predicted contrasts needed for safe
detection.
CONCLUSION
As previous experimental data and results[1] show, the CHC2 contrast metric[1] can be used
to predict mesopic detection threshold contrasts effectively. By using the CHC2 contrast
metric, the spectral responsivity of the human retina in the mesopic range is well described as
it takes the spectrally non-additive contribution of the chromatic channels into account.
Mesopic visual performance models working with spectral integration do not account for the
influence of the chromatic (i.e. spectrally opponent) channels correctly, hence spectral nonadditivity causes a problem. This is the case for every white or near-white like source used
e.g. in street lighting. If a spectrally composite light source is used, non-additivity occurs,
caused by the chromatic influence. Neither the VMP(λ) alone or any achromatic model can
solve this problem. The above spectral integration models (chromatic or achromatic) may
cause an under- or overestimation of mesopic detection sensitivity. If mesopic detection
sensitivity is underestimated then lower predicted detection probability values are predicted
than the real probability of detection near the detection threshold. This is only a problem from
an economic point of view: more energy is used for lighting. Overestimation of detection
sensitivity is more critical because in this case the predicted threshold is lower than the real
contrast threshold needed for safe detection. The CHC2 model predicts the effect of spectral
non-additivity in the mesopic range. This results in more accurate detection contrast threshold
values and to a safe mesopic object detection condition.
LITERATURE
[1] Bodrogi P. Vas Z. Haferkemper N. Varady G. Schiller C. Khanh TQ. Schanda J. Effect of
chromatic mechanisms on the detection of mesopic incremental targets at different
eccentricities. Ophthalmic and Physiological Optics. Vol. 30. 85-94. 2010.
[2] Sagawa K. Toward a CIE supplementary system of photometry: brightness at any level
including mesopic vision. Ophthalmic and Physiological Optics. 26. pp. 240–245. 2006.
[3] Rea. M.S and Bullough. J. D..: Making the move to a unified system of photometry.
Lighting Research and Technology 39/4. pp. 393-408. 2007.
[4] Goodman T. Forbes A. Walkey H. Eloholma M. Halonen L. Alferdinck J. Freiding A.
Bodrogi P. Varady G. Szalmas A. Mesopic visual efficiency IV: A model with relevance to
night-time driving and other applications. Lighting Research and Technology 39/4. pp.365392. 2007.
[5] Rea M.S. Bullough J.D. Freyssinier N.J.P. Bierman A: A proposed unified system of
photometry. Lighting Research and Technology. 36/2 pp. 51-111. 2004.
[6] CIE TC 1-58 draft Technical Report “Recommended system for visual performance based
mesopic photometry.Draft 3. 2009.
[7] Walkey H. Orrevetelainen P. Barbur J. Halonen L. Goodman T. Alferdinck J.. Freiding A.
Szalmas A: Mesopic visual efficiency II: reaction time experiments. Lighting Research and
Technology. 39(4): 335 - 354. 2007
[8] McGowan T. Rea M.S.: Visibility and spectral composition: Another look in the mesopic.
70 Years of CIE Photometry. Comission Internationale de l’Eclairage Proceedings. Vienna.
CIE X009-1995.
[9] Szabó F. Vas Z. Schanda J: Investigation of the effect of light source spectra on visual
acuity at mesopic lighting conditions. Accepted paper at CIE Conference: “Lighting Quality
and Energy Efficiency”. Vienna 2010.
[10] Haferkemper N. Frohnapfel A. Paramei G. Khanh TQ. A mesopic experiment series at
automotive visual conditions. Proc. 7th International Symposium on Automotive Lighting.
Darmstadt. pp. 402-409. 2007.
[11] Padmos P. Norren D.V.: Increment spectral responsivity and colour discrimination in the
primate studies by means of graded potentials from the striate cortex. Vision Research 15:
1103-1113. 1975.
[12] García-Pérez M. A.: Forced-choice staircase with fixed step sizes: asymptotic and smallsample properties. Vision Research 38. pp. 1861-1881. 1998.
[13] Stockman A. Sharpe L T: Spectral sensitivities of the middle- and long-wavelength
sensitive cones derived from measurements in observers of known genotype. Vision
Research. 40. 1711-1737. 2000.
[14] Kurtenbach A. Meierkord S. Kremers J: Spectral sensitivities in dichromats and
trichromats at mesopic retinal illuminances. J. Opt. Soc. Am. A 16. 1541–1547. 1999.
[15] Stockman A: The dependence of luminous efficiency on chromatic adaptation. CIE
“Light and Lighting 2009” Midterm Conference. CD proceedings. 2009.
[16] http://vision.uni-pannon.hu/~vasz/experimentaldata.rar
Zoltan Vas:
Virtual Environment and Imaging Technologies Laboratory.
University of Pannonia. Egyetem u. 10. H-8200 Veszprém. Hungary
@: {vas.zoltaan@gmail.com}
Peter Bodrogi:
FG Lichttechnik. Technische Universität Darmstadt.
Hochschulstraße 4a. D-64289 Darmstadt. Germany
@: {bodrogi@lichttechnik.tu-darmstadt.de}
Janos Schanda:
Virtual Environment and Imaging Technologies Laboratory.
University of Pannonia. Egyetem u. 10. H-8200 Veszprém. Hungary
@: {schandaj@gmail.com}
Geza Varady:
Pollack Mihály Faculty of Engineering. Dept. of System- and Software Technologies.
University of Pécs. Boszorkány u. 2. H-7624 Pécs. Hungary
@: {varadygeza@gmail.com}