CN107024340A - The illumination preference degree evaluation index construction method and system optimized based on color card - Google Patents
The illumination preference degree evaluation index construction method and system optimized based on color card Download PDFInfo
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Abstract
A kind of illumination preference degree evaluation index construction method optimized based on color card and system, including build typical color sample spectrum reflectivity data collection；Build full gamut typical color sample set；Collect existing representative illumination preference degree data multinomial, and obtain it and use light source relative spectral power distributions and correspondence preference degree sorting data；Some color cards are chosen from above-mentioned full gamut typical color sample set, its all combination is tested, and for the gamut volume of specific color sample combination under each light conditions of each combination solution；Required SPEARMAN coefficient correlations between gamut volume and corresponding preference degree sorting data are calculated, and and then according to meta-analysis method, the aggregative weighted coefficient correlation of the corresponding coefficient correlation of solution each group research；Foundation is turned to the aggregative weighted coefficient correlation maximum, optimal color sample combination, and the gamut volume with the combined sample under light conditions to be measured is determined, is used as final index.
Description
Technical field
The invention belongs to lighting quality assessment technique field, and in particular to a kind of illumination hobby optimized based on color card
Spend evaluation index construction method and system.
Background technology
Lighting quality evaluates significant, the scientific and reasonable lighting quality evaluation side of the development for lighting industry
Method, is the prerequisite for instructing illuminating product design and production.At present, continuing to develop with lighting engineering, industrial quarters and
Academia's multi-dimensional nature that generally approval lighting quality is evaluated, that is, evaluate the lighting quality of light source, should include photoreduction, happiness
Multiple dimensions such as good degree, color discrimination, naturalness.
Among above-mentioned dimension, illumination preference degree is a big focus of industry research in recent years, because it is more comprehensive complete
Face and intuitively reflect fancy grade of the consumer to light source product.At present, in terms of illumination preference degree evaluation index structure,
Most researchers are by specific experiment (certain observation object, specific user colony, specific light source product), so as to obtain
Go out qualitative or quantitative relevant conclusion.
Bibliography 1：Wang Q,Xu H,Zhang F,et al.Influence of color temperature on
comfort and preference for LED indoor lighting.Optik 2017；129:21-29
Bibliography 2：SzabóF,Kéri R,Schanda J,et al.A study of preferred colour
rendering of light sources:Home lighting.Lighting Research&Technology 2016；
48:103-125
However, because illumination hobby is extremely complex visually-perceptible process, it is related to light measurement, colorimetry and cognition
The numerous areas such as science, therefore the hobby evaluation procedure is influenceed by many factors, such as observer colony, observation object.Herein
In the case of kind, the research conclusion that single research institute obtains often has one-sidedness, although that is, it can be very good explanation and itself grinds
Study carefully experimental data, but for other correlative study data, then can not reasonable dismissal.
Therefore, in research application field at present, existing researcher is directed to the relevant number by comprehensive multinomial research work
According to so as to more comprehensively be tested and assessed to existing light source evaluation index.
Bibliography 3.Smet K, Ryckaert W, Pointer MR, et al.A memory colour quality
metric for white light sources.Energy and Buildings 2012；49:216-225.
However, being restricted by subjective and objective factors such as theoretical method levels, above-mentioned work is also limited only to existing illumination evaluation
In terms of the comprehensive test of index, it there is no method according to collected multinomial data, completes new illumination preference degree evaluation and refers to
Target construction work.For problem above, academic circles at present not yet proposes corresponding solution to industrial quarters.
The content of the invention
The invention aims to solve problem described in background technology, a kind of illumination hobby of more universality is proposed
Spend evaluation index construction method and system.
The technical scheme is that providing a kind of illumination preference degree evaluation index structure side optimized based on color card
Method, comprises the following steps：
Step 1, typical color sample is chosen, with the spectral reflectance data in the typical color sample visible-range
Constitute typical color sample spectrum reflectivity data collection；
Step 2, under D50/2 chrominance requirements, each sample correspondence dominant wavelength information in step 1 sample set is asked for, and with master
Wavelength is that foundation is classified as M classes；
Step 3, according to the variable sex index of color, the poor sample of color fastness in above-mentioned sample is rejected, that is, rejects color easy
Degenerative index is unsatisfactory for CMCCON02 ＜ 8 sample；
Step 4, under D50/2 chrominance requirements, using excitation as foundation, excitation maximum in all kinds of color samples is asked for
Sample, is constituted in final full gamut sample set G, wherein G containing M sample；
Step 5, ask for appointing the whole combinations for taking N number of sample, wherein N ＜ M from M sample in full gamut sample set G；
Step 6, representative illumination preference degree data S is collected, it specifically includes light source preference degree and evaluates ordinal number P
Group, every group is evaluated ordinal number and each corresponding light source relative spectral power distributions information, the light source comprising some light source preference degrees
Preference degree is evaluated ordinal number and arranged according to ascending order；
Step 7, all of N number of sample are taken for striked appointing from M sample in full gamut sample set G in step 5
Combined situation, with reference to each light source relative spectral power distributions information in step 6, is asked under whole combination conditions, N number of sample is each
In the gamut volume of CIELAB color spaces under light source lighting condition；
Step 8, for each group of data in S group research work collected in step 6, and N number of sample in step 5
Whole combined situations, P group light sources preference degree mentioned in calculation procedure 6 evaluates ordinal number and each light striked by step 7
SPEARMAN correlation coefficient rs between the gamut volume of source；
Step 9, combined for whole color samples in step 5, solve the aggregative weighted phase relation based on meta-analysis
Number R；
Step 10, the aggregative weighted coefficient R maximum solved with step 9 turns to principle, determines optimal color sample group
Close O；
Step 11, for any light source to be tested and assessed, for its relative spectral power distributions, ask for determining it in step 10
Color sample combines O in the gamut volume of CIELAB color spaces, and as final light shines preference degree evaluation index value.
Moreover, sample set classification quantity M values span is 18≤M≤20 in step 2, data set is entered with dominant wavelength
Dominant wavelength is divided into 3 groups for all samples of negative value during row packet, afterwards put down other samples by foundation of dominant wavelength
It is grouped, is divided into M-3 groups；N values span is 14≤N≤16 and N ＜ M-3 in step 5.
Moreover, representative studies quantity S spans are S ＞ 6 in step 6.
Moreover, solved in step 7 and step 11 in CIELAB color spaces the method for colour gamut using algorithm of convex hull or α-
Shape algorithms.
Moreover, the formula in step 9 using meta-analysis solution aggregative weighted coefficient R is as follows,
Wherein, P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiEvaluated for i-th group of light source preference degree
The product of light source species and observer's number employed in ordinal number, by step 6) obtain, riEvaluated for i-th group of light source preference degree
SPEARMAN coefficient correlations between ordinal number and each light source gamut volume.
The present invention provides a kind of illumination preference degree evaluation index constructing system optimized based on color card, including following mould
Block：
Typical color sample spectrum data set builds module, for choosing typical color sample, with the typical color sample
Spectral reflectance data in this visible-range constitutes typical color sample spectrum reflectivity data collection；
Dominant wavelength sort module, mould is built under D50/2 chrominance requirements, asking for typical color sample spectrum data set
Each sample correspondence dominant wavelength information in block sample set, and it is classified as M classes by foundation of dominant wavelength；
Color fastness optimization module, for according to the variable sex index of color, rejecting the poor sample of color fastness in above-mentioned sample,
Reject the sample that the variable sex index of color is unsatisfactory for CMCCON02 ＜ 8；
Full gamut sample set builds module, under D50/2 chrominance requirements, using excitation as foundation, asks for all kinds of colors
The maximum sample of excitation in color sample, is constituted in final full gamut sample set G, wherein G containing M sample；
Sample group closes computing module, for asking for appointing the whole for taking N number of sample from M sample in full gamut sample set G
Combination, wherein N ＜ M；
Correlative study data collection module, for collecting representative illumination preference degree data S, it specifically includes light
Source preference degree evaluates ordinal number P groups, and every group is evaluated ordinal number and each corresponding light source relative spectral power comprising some light source preference degrees
Distributed intelligence；
Gamut volume computing module, for closing striked from full gamut sample set G in computing module for sample group
M sample appoint and take all combined situations of N number of sample, with reference to each light source relative spectral work(in correlative study data collection module
Rate distributed intelligence, is asked under whole combination conditions, and N number of sample is under each light source lighting condition in the color of CIELAB color spaces
Domain volume；
Coefficient correlation computing module, for in S item research work collected in correlative study data collection module
Each group of data, and sample group close computing module in N number of sample whole combined situations, calculate correlative study Data Collection
Each light source gamut volume striked by P group light sources preference degree evaluation ordinal number and gamut volume computing module mentioned in module
Between SPEARMAN correlation coefficient rs；
Aggregative weighted coefficient correlation computing module, for closing whole color sample groups in computing module for sample group
Close, solve the aggregative weighted coefficient R based on meta-analysis；
Optimal color sample selecting module, the aggregative weighted for being solved with aggregative weighted coefficient correlation computing module is related
Coefficients R maximum turns to principle, determines optimal color sample combination O；
Gamut volume computing module, for any light source to be tested and assessed, for its relative spectral power distributions, asks for optimal color
The color sample combination O determined in color sample selection module shines in the gamut volume of CIELAB color spaces, as final light
Preference degree evaluation index value.
Moreover, sample set classification quantity M values span is 18≤M≤20 in dominant wavelength sort module, with dominant wavelength
Dominant wavelength is divided into 3 groups for all samples of negative value when being grouped to data set, afterwards by other samples using dominant wavelength as
According to average packet is carried out, it is divided into M-3 groups；It is 14≤N≤16 and N ＜ M- that sample group, which closes N values span in computing module,
3。
Moreover, representative studies quantity S spans are S ＞ 6 in correlative study data collection module.
Moreover, solving color in CIELAB color spaces in gamut volume computing module and optimal color sample selecting module
The method of domain volume uses algorithm of convex hull or α-shape algorithms.
Moreover, the public affairs of aggregative weighted coefficient R are solved in aggregative weighted coefficient correlation computing module using meta-analysis
Formula is as follows,
Wherein P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiEvaluated for i-th group of light source preference degree
The product of light source species and observer's number, is obtained, r by correlative study data collection module employed in ordinal numberiFor i-th group
Light source preference degree evaluates the SPEARMAN coefficient correlations between ordinal number and each light source gamut volume.
Compared with prior art, beneficial effects of the present invention are as follows：
A kind of illumination preference degree evaluation index constructing technology scheme optimized based on color card proposed by the present invention, with color
Color sample optimization is technological approaches, and construction method is quantified as with sample gamut volume, effective by the meta-analysis of coefficient correlation
Combine the achievement in research in existing field, so as to ensure that the comprehensive and robustness of constructed index.The method is more managed
That thinks solves background section described problem, thereby may be ensured that the scientific rationality of light source preference degree prediction, and implement
It is convenient, there is stronger applicability in illumination quality evaluation field.Because technical solution of the present invention has important application meaning, by
To multiple project supports：1. the youth's morning twilight talent's plan of the Wuhan City of project of national nature science fund project 61505149,2.
2016070204010111,3. Hubei Province Nsfc Projects 2015CFB204,4 Shenzhen's basic research projects
JCYJ20150422150029093.Technical solution of the present invention is protected, will be leading in the world to China's relevant industries competition
Position is significant.
Brief description of the drawings
Fig. 1 is the flow chart of the embodiment of the present invention.
Fig. 2 is 14 optimal color sample spectral reflectivity figures finally choosing in the embodiment of the present invention.
Embodiment
It is described in detail below with reference to accompanying drawing there is provided the embodiment of the present invention.
A kind of illumination preference degree evaluation index constructing technology optimized based on color card that embodiment as shown in Figure 1 is provided
Scheme, technological approaches is optimized for color sample, and construction method is quantified as with sample gamut volume, passes through assembling for coefficient correlation
Analysis effectively combines the achievement in research in existing field, so as to ensure that the comprehensive and robustness of constructed index.This side
Method it is ideal solve background section described problem, thereby may be ensured that light source preference degree prediction it is scientific and reasonable
Property, and it is easy to implement, there is stronger applicability in illumination quality evaluation field.
Embodiment builds typical color sample sets using 8560 color samples, using 8 groups of existing research datas to grind
Study carefully object, build illumination preference degree index, and by itself and CIE Color Rendering Index (CRI), Gamut Area
Index(GAI),Full Spectrum Colour Index(FSCI),Colour Quality Scale(CQS:Qa,Qf,
Qp,Qg),Feeling of Contrast Index(FCI),Colour Discrimination Index(CDI),Cone
Surface Area(CSA),Color Preference Index(CPI),CRI-CAM02UCS,CRI2012,IES TM-30
(Rf and Rg), it is pre- that Memory Colour Rendering Index (MCRI) etc. amount to 16 kinds of existing classical index preference degrees
Precision is surveyed to be compared.Because bibliography is more, do not provide one by one herein, those skilled in the art can easily pass through its title
Retrieve relevant technical details.It should be noted that the invention is not limited in light source, object and observation involved by the studies above
Person colony, the preference degree index for other light scenes is built, and this method is equally applicable.
Technical solution of the present invention can be realized automatic by those skilled in the art using computer software technology when being embodied
Operation.The method flow that embodiment is provided comprises the following steps：
1) typical color sample is chosen, allusion quotation is constituted with the spectral reflectance data in each typical color sample visible-range
Type color sample spectral reflectance data collection；
Embodiment uses typical case's color sample spectrum reflectivity data collection that prior art has been announced, and it includes and is uniformly distributed
Full gamut sample colo(u)r atlas 8560, wave-length coverage 400nm-700nm.Refer to bibliography：Liu Q,Wan X,Liang J,
et al.Neural network approach to a colorimetric value transform based on a
large scale spectral dataset.Coloration Technology 2017；133:73-80
2) under D50/2 chrominance requirements, each sample correspondence dominant wavelength information in 1) sample set is asked for, and on this basis will
It is divided into M classes；Moreover, sample set classification quantity M values span is 18≤M≤20, data set is divided with dominant wavelength
Dominant wavelength is divided into 3 groups for all samples of negative value during group, afterwards by other samples using dominant wavelength as according to progress average mark
Group；It is divided into M-3 groups.
In embodiment, M values are 18, i.e., dominant wavelength is divided into 3 groups for all samples of negative value, afterwards by other samples
This divides equally 15 groups using dominant wavelength as according to progress.Wherein, dominant wavelength packet node is respectively in embodiment：-505nm,-
553nm,-569nm,0nm,468nm,478nm,485nm,491nm,499nm,518nm,549nm,565nm,573nm,579nm,
585nm,592nm,601nm,612nm.Wherein, dominant wavelength computational methods can be found in J.Schanda.CIE
Colorimetry.Wiley Online Library, 2007, it will not go into details by the present invention.
3) according to the variable sex index (CMCCON02 ＜ 8) of color；Reject the poor sample of color fastness in above-mentioned sample；
In embodiment, altogether in 8560 samples from 1), the color sample for being unsatisfactory for (CMCCON02 ＜ 8) condition is rejected
This 1054.Wherein CMCCON02 indexes are calculated as prior art, reference can be made to：Luo M,Rigg B and Smith K.CMC
2002colour inconstancy index；CMCCON02.Coloration Technology 2006；119:280-285,
It will not go into details by the present invention.
4) under D50/2 chrominance requirements, using excitation as foundation, the maximum sample of excitation in all kinds of color samples is asked for
This, constitutes in final full gamut sample set G, wherein G containing M sample；
In embodiment, using excitation as foundation, ultimately generate in full gamut sample set G, wherein G containing 18 samples.
Wherein, excitation computational methods can be found in J.Schanda.CIE colorimetry.Wiley Online Library, and 2007,
It will not go into details by the present invention.
5) ask for appointing the whole combinations (N ＜ M) for taking N number of sample from M sample in full gamut sample set G, moreover, N values
Span is 14≤N≤16 and N ＜ M-3.
In embodiment, M values 18, N values 14, therefore appoint herein from M sample and take whole combinations of N number of sample shared
3060 kinds.
6) representative illumination preference degree data S is collected, it specifically includes light source preference degree and evaluates ordinal number P groups, often
Group evaluates ordinal number (ascending order is arranged, and it is big that preference degree evaluates high person's numerical sequence) and each corresponding light source phase comprising some light source preference degrees
To spectral power distribution information；Moreover, S spans are S ＞ 6.
In embodiment, representative illumination preference degree data S=8 is collected altogether, because of possible bag in each research
Scene (such as art work scene, fruit and vegetable scene) is evaluated containing multiple illuminations, therefore amounts to and obtains light source preference degree evaluation sequence
Number P=32 groups, while collecting P group light sources preference degree evaluates the letter such as light source species and observer's number of every group of use in ordinal number
Breath.Wherein, 8 researchs that embodiment is used can be found in bibliography respectively：
A.M.Wei,K.W.Houser,G.R.Allen,and W.W.Beers,LEUKOS 10,119-131(2014).
B.M.Royer,A.Wilkerson,M.Wei,K.Houser,and R.Davis,Lighting Research&
Technology,1477153516663615(2016).
C.S.Jost-Boissard,M.Fontoynont,and J.Blanc-Gonnet,Journal of Modern
Optics 56,1420-1432(2009)
D.S.Jost-Boissard,P.Avouac,and M.Fontoynont,Lighting Research&
Technology47(2014)
E.F.Szabó,R.Kéri,J.Schanda,P.Csuti,and E.Mihálykó-Orbán,Lighting
Research&Technology 48,103-125(2016).
F.Z.Huang,Q.Liu,S.Westland,M.R.Pointer,M.R.Luo,and K.Xiao,Lighting
research and technology(2017)
G.N.Narendran and L.Deng,International Society for Optics and
Photonics,2002,61-67
H.Q.Wang,H.Xu,F.Zhang,and Z.Wang,Optik 129,21-29(2017)
Wherein, Data Collection mode can be by obtaining to related author's communication contact, also can be directly from related research paper
Extract and obtain.
7) striked all combination feelings that N number of sample is taken from M sample in full gamut sample set G times in being directed to 5)
Condition, with reference to each light source relative spectral power distributions information in 6), is asked under whole combination conditions, N number of sample is illuminated in each light source
Under the conditions of in the gamut volume of CIELAB color spaces, wherein the method for colour gamut gamut volume uses algorithm of convex hull or α-shape
Algorithm.
In embodiment, 14 samples are chosen from 18 full gamut samples, 3060 kinds of combinations are had.Then for 6) Zhong Geguang
Source relative spectral power distributions information, can try to achieve 3060 kinds of gamut volumes altogether.Wherein, CIELAB color spaces, algorithm of convex hull and
α-shape algorithms are prior art, and it will not go into details herein.
8) for each group of data in collected S item research work in 6), and 5) in the whole of N number of sample combine
Situation, calculate 6) mentioned in P group light sources preference degree evaluation ordinal number and 7) striked by each light source gamut volume between
SPEARMAN correlation coefficient rs；
, all can be according to convex closure described in 7) for any one of 3060 kinds of samples combination described in 5) in embodiment
Algorithm or α-shape algorithms (embodiment specifically uses algorithm of convex hull), the colour gamut corresponding to each light source mentioned in calculating 6)
Volume.Then, ordinal number and corresponding light source gamut volume are evaluated for the P group light sources preference degree referred in 6), can calculates and obtain
Correspondence SPEARMAN correlation coefficient rs.In the process, because light source gamut volume has 3060 kinds, light source preference degree evaluates ordinal number
There are P=32 groups, so step, which amounts to, calculates 3060*32=97920 group SPEARMAN correlation coefficient rs.
9) whole color samples combination in being directed to 5), solves the aggregative weighted coefficient R based on meta-analysis, formula
As follows, wherein P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source species and observer's number product, by step 6) obtain, riOrdinal number is evaluated for i-th group of light source preference degree
With the SPEARMAN coefficient correlations between each light source gamut volume.
In embodiment, this step is that 3060*32=97920 group SPEARMAN correlation coefficient rs is public by above-mentioned weighting
Formula, comprehensive for 3060 groups of aggregative weighted coefficient Rs, one group of sample during 5) each of which group R all corresponds to is combined.
10) principle is turned to the aggregative weighted coefficient R maximum 9) solved, determines optimal color sample combination O；
In embodiment, the maximum (R=0.83) of required 3060 groups of aggregative weighted coefficient Rs in choosing 9), its is right
It is optimal color sample combination O to answer 14 color samples, and the spectral reflectivity curve of 14 color samples is as shown in Figure 2.
11) for any light source to be tested and assessed, for its relative spectral power distributions, the color sample determined in asking for 10)
This combination O gamut volume, as final light shine preference degree evaluation index value.
Embodiment by taking certain fluorescent light as an example, by asking for 10) in determined by 14 groups of optimization samples under this light source
In the gamut volume of CIELAB color spaces, its final light is obtained according to preference degree evaluation index value：14.92.Preference degree and face
The saturation degree of color has relation, and gamut volume is the efficiency index for representing color saturation, therefore, it can use gamut volume conduct
Illumination preference degree evaluation index value, in general, gamut volume is bigger, represents that color gets over saturation, illumination preference degree evaluation refers to
Scalar value is higher, wherein, it is the basic general knowledge of colourity theory that gamut volume, which solves correlation technique, and it will not go into details by the present invention.
Further to confirm the technical advantage that the method for the invention has in terms of illumination preference degree evaluation, use
CIE Color Rendering Index(CRI),Gamut Area Index(GAI),Full Spectrum Colour
Index(FSCI),Colour Quality Scale(CQS:Qa,Qf,Qp,Qg),Feeling of Contrast Index
(FCI),Colour Discrimination Index(CDI),Cone Surface Area(CSA),Color
Preference Index(CPI),CRI-CAM02UCS,CRI2012,IES TM-30(Rf and Rg),Memory Colour
Rendering Index (MCRI) etc. amount to 16 kinds of existing classical indexs, according to 8) 9) described in calculate aggregative weighted phase relation
Number R method, calculates the preference degree precision of prediction for 8 research that 16 kinds of existing classical indexs are used for embodiment.(due to
Bibliography is more, does not provide one by one herein, and those skilled in the art can easily pass through its title, and to retrieve correlation technique thin
Section).As a result show, aggregative weighted coefficient R maximum obtained by this 16 kinds classical indexs is R (GAI)=0.54, far below this
The described method R=0.83 of invention.
The present invention also provides a kind of illumination preference degree evaluation index constructing system optimized based on color card, including following
Module：
Typical color sample spectrum data set builds module, for choosing typical color sample, with each typical color sample
Spectral reflectance data in visible-range constitutes typical color sample spectrum reflectivity data collection；
Dominant wavelength sort module, mould is built under D50/2 chrominance requirements, asking for typical color sample spectrum data set
Each sample correspondence dominant wavelength information in block sample set, and M classes are classified as on this basis；
Color fastness optimization module, for according to the variable sex index (CMCCON02 ＜ 8) of color；Reject color jail in above-mentioned sample
The poor sample of degree；
Full gamut sample set builds module, under D50/2 chrominance requirements, using excitation as foundation, asks for all kinds of colors
The maximum sample of excitation in color sample, is constituted in final full gamut sample set G, wherein G containing M sample；
Sample group closes computing module, for asking for appointing the whole for taking N number of sample from M sample in full gamut sample set G
Combine (N ＜ M)；
Correlative study data collection module, for collecting representative illumination preference degree data S, it specifically includes light
Source preference degree evaluates ordinal number P groups, and every group is evaluated ordinal number (ascending order arrangement, preference degree evaluation Gao Zhexu comprising some light source preference degrees
Numerical value is big) and each corresponding light source relative spectral power distributions information；
Gamut volume computing module, for closing striked from full gamut sample set G in computing module for sample group
M sample appoint and take all combined situations of N number of sample, with reference to each light source relative spectral work(in correlative study data collection module
Rate distributed intelligence, is asked under whole combination conditions, and N number of sample is under each light source lighting condition in the colour gamut of CIELAB color spaces
Volume；
Coefficient correlation computing module, for in S group research work collected in correlative study data collection module
Each group of data, and sample group close computing module in N number of sample whole combined situations, calculate correlative study Data Collection
Light source preference degree mentioned in module evaluate ordinal number and each light source gamut volume striked by gamut volume computing module it
Between SPEARMAN correlation coefficient rs；
Aggregative weighted coefficient correlation computing module, for closing whole color sample groups in computing module for sample group
Close, solve the aggregative weighted coefficient R based on meta-analysis；
Optimal color sample selecting module, the aggregative weighted for being solved with aggregative weighted coefficient correlation computing module is related
Coefficients R maximum turns to principle, determines optimal color sample combination O；
Gamut volume computing module, for any light source to be tested and assessed, for its relative spectral power distributions, asks for optimal color
The color sample combination O determined in color sample selection module shines in the gamut volume of CIELAB color spaces, as final light
Preference degree evaluation index value.
Wherein, sample set classification quantity M values span is 18≤M≤20 in dominant wavelength sort module.With dominant wavelength
Dominant wavelength is divided into 3 groups for all samples of negative value when being grouped to data set, afterwards by other samples using dominant wavelength as
According to progress average packet；It is divided into M-3 groups.
Wherein, it is 14≤N≤16 and N ＜ M-3 that sample group, which closes N values span in computing module,.
Wherein, representative studies quantity S spans are S ＞ 6 in correlative study data collection module.
Wherein, the method that gamut volume is solved in gamut volume computing module and optimal color sample selecting module is used
Algorithm of convex hull or α-shape algorithms.
Wherein, the public affairs of aggregative weighted coefficient R are solved in aggregative weighted coefficient correlation computing module using meta-analysis
Formula is as follows, and wherein P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiSequence is evaluated for i-th group of light source preference degree
The product of light source species and observer's number, is obtained, r by correlative study data collection module employed in numberiFor i-th group of light
Source preference degree evaluates the SPEARMAN coefficient correlations between ordinal number and each light source gamut volume.
Each module is implemented with each step accordingly, and it will not go into details by the present invention.
Specific embodiment described herein is only to spirit explanation for example of the invention.Technology neck belonging to of the invention
The technical staff in domain can be made various modifications or supplement to described specific embodiment or be replaced using similar mode
Generation, but without departing from the spiritual of the present invention or surmount scope defined in appended claims.
Claims (10)
1. a kind of illumination preference degree evaluation index construction method optimized based on color card, it is characterised in that including following step
Suddenly：
Step 1, typical color sample is chosen, is constituted with the spectral reflectance data in the typical color sample visible-range
Typical color sample spectrum reflectivity data collection；
Step 2, under D50/2 chrominance requirements, each sample correspondence dominant wavelength information in step 1 sample set is asked for, and with dominant wavelength
M classes are classified as foundation；
Step 3, according to the variable sex index of color, the poor sample of color fastness in above-mentioned sample is rejected, that is, rejects color mutability
Index is unsatisfactory for CMCCON02 ＜ 8 sample；
Step 4, under D50/2 chrominance requirements, using excitation as foundation, the maximum sample of excitation in all kinds of color samples is asked for
This, constitutes in final full gamut sample set G, wherein G containing M sample；
Step 5, ask for appointing the whole combinations for taking N number of sample, wherein N ＜ M from M sample in full gamut sample set G；
Step 6, representative illumination preference degree data S is collected, it specifically includes light source preference degree and evaluates ordinal number P groups, often
In group ordinal number and each corresponding light source relative spectral power distributions information are evaluated comprising some light source preference degrees；
Step 7, all combinations of N number of sample are taken for striked appointing from M sample in full gamut sample set G in step 5
Situation, with reference to each light source relative spectral power distributions information in step 6, is asked under whole combination conditions, N number of sample is in each light source
In the gamut volume of CIELAB color spaces under lighting condition；
Step 8, for each group of data in S item research work collected in step 6, and N number of sample in step 5 is complete
Portion's combined situation, each light source colour striked by P group light sources preference degree evaluation ordinal number and step 7 mentioned in calculation procedure 6
SPEARMAN correlation coefficient rs between the volume of domain；
Step 9, combined for whole color samples in step 5, solve the aggregative weighted coefficient R based on meta-analysis；
Step 10, the aggregative weighted coefficient R maximum solved with step 9 turns to principle, determines optimal color sample combination O；
Step 11, for any light source to be tested and assessed, for its relative spectral power distributions, the color determined in step 10 is asked for
Sample combines O in the gamut volume of CIELAB color spaces, and as final light shines preference degree evaluation index value.
2. a kind of illumination preference degree evaluation index construction method optimized based on color card according to claim 1, its
It is characterised by：
Sample set classification quantity M values span is 18≤M≤20 in step 2, when being grouped with dominant wavelength to data set
Dominant wavelength is divided into 3 groups for all samples of negative value, afterwards divided other samples using dominant wavelength as according to average packet is carried out
For M-3 groups；N values span is 14≤N≤16 and N ＜ M-3 in step 5.
3. a kind of illumination preference degree evaluation index construction method optimized based on color card according to claim 1 or 2,
It is characterized in that：Representative studies quantity S spans are S ＞ 6 in step 6.
4. a kind of illumination preference degree evaluation index construction method optimized based on color card according to claim 3, its
It is characterised by：Solved in step 7 and step 11 in CIELAB color spaces the method for gamut volume using algorithm of convex hull or α-
Shape algorithms.
5. a kind of illumination preference degree evaluation index construction method optimized based on color card according to claim 4, its
It is characterised by：Formula in step 9 using meta-analysis solution aggregative weighted coefficient R is as follows,
Wherein, P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source species and observer's number product, by step 6) obtain, riOrdinal number is evaluated for i-th group of light source preference degree
With the SPEARMAN coefficient correlations between each light source gamut volume.
6. a kind of illumination preference degree evaluation index constructing system optimized based on color card, it is characterised in that including following mould
Block：
Typical color sample spectrum data set builds module, can with the typical color sample for choosing typical color sample
The spectral reflectance data seen in optical range constitutes typical color sample spectrum reflectivity data collection；
Dominant wavelength sort module, module sample is built under D50/2 chrominance requirements, asking for typical color sample spectrum data set
Each sample correspondence dominant wavelength information of this concentration, and it is classified as M classes by foundation of dominant wavelength；
Color fastness optimization module, for according to the variable sex index of color, rejecting the poor sample of color fastness in above-mentioned sample, that is, picking
Except the variable sex index of color is unsatisfactory for CMCCON02 ＜ 8 sample；
Full gamut sample set builds module, under D50/2 chrominance requirements, using excitation as foundation, asks for all kinds of color samples
The maximum sample of excitation in this, is constituted in final full gamut sample set G, wherein G containing M sample；
Sample group closes computing module, for asking for appointing the whole combinations for taking N number of sample from M sample in full gamut sample set G,
Wherein N ＜ M；
Correlative study data collection module, for collecting representative illumination preference degree data S, it specifically includes light source happiness
Good degree is evaluated in ordinal number P groups, every group comprising some light source preference degrees evaluation ordinal number and corresponding light source relative spectral power distributions
Information；
Gamut volume computing module, for closing striked from M in full gamut sample set G in computing module for sample group
Sample appoints all combined situations for taking N number of sample, with reference to each light source relative spectral power in correlative study data collection module point
Cloth information, is asked under the conditions of each combination, N number of sample is under each light source lighting condition in the gamut volume of CIELAB color spaces；
Coefficient correlation computing module, for for each in S item research work collected in correlative study data collection module
Group data, and sample group close whole combined situations of N number of sample in computing module, calculate correlative study data collection module
Mentioned in P group light sources preference degree evaluate between ordinal number and each light source gamut volume striked by gamut volume computing module
SPEARMAN correlation coefficient rs；
Aggregative weighted coefficient correlation computing module, whole color samples for being closed for sample group in computing module are combined, and are asked
Aggregative weighted coefficient R of the solution based on meta-analysis；
Optimal color sample selecting module, for the aggregative weighted coefficient correlation solved with aggregative weighted coefficient correlation computing module
R maximums turn to principle, determine optimal color sample combination O；
Gamut volume computing module, for any light source to be tested and assessed, for its relative spectral power distributions, asks for optimal color sample
The color sample combination O determined in this selecting module is in the gamut volume of CIELAB color spaces, and as final light is according to hobby
Spend evaluation index value.
7. a kind of illumination preference degree evaluation index constructing system optimized based on color card according to claim 6, its
It is characterised by：
Sample set classification quantity M values span is 18≤M≤20 in dominant wavelength sort module, and data set is entered with dominant wavelength
Dominant wavelength is divided into 3 groups for all samples of negative value during row packet, afterwards put down other samples by foundation of dominant wavelength
It is grouped, is divided into M-3 groups；
It is 14≤N≤16 and N ＜ M-3 that sample group, which closes N values span in computing module,.
8. a kind of illumination preference degree evaluation index constructing system optimized based on color card according to claim 6 or 7,
It is characterized in that：Representative studies quantity S spans are S ＞ 6 in correlative study data collection module.
9. a kind of illumination preference degree evaluation index constructing system optimized based on color card according to claim 8, its
It is characterised by：In gamut volume computing module and optimal color sample selecting module colour gamut body is solved in CIELAB color spaces
Long-pending method uses algorithm of convex hull or α-shape algorithms.
10. a kind of illumination preference degree evaluation index constructing system optimized based on color card according to claim 9, its
It is characterised by：In aggregative weighted coefficient correlation computing module the formula of aggregative weighted coefficient R is solved using meta-analysis such as
Under,
Wherein, P is the group number of contained light source preference degree evaluation ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source species and observer's number product, obtained by correlative study data collection module, riFor i-th group of light source
Preference degree evaluates the SPEARMAN coefficient correlations between ordinal number and each light source gamut volume.
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