CN107024340B - Illumination preference degree evaluation index construction method and system based on color card optimization - Google Patents
Illumination preference degree evaluation index construction method and system based on color card optimization Download PDFInfo
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Abstract
A kind of illumination preference degree evaluation index construction method and system based on color card optimization, including construct typical color sample spectrum reflectivity data collection；Construct full gamut typical color sample set；It is multinomial to collect existing representative illumination preference degree data, and obtains it and uses light source relative spectral power distributions and corresponding preference degree sorting data；Several color cards are chosen from above-mentioned full gamut typical color sample set, test its all combination, and solve the gamut volume of specific color sample combination under each light conditions for each combination；Required gamut volume and the SPEARMAN related coefficient between corresponding preference degree sorting data are calculated, and solves the aggregative weighted related coefficient of related coefficient corresponding to each group research according to meta-analysis method in turn；Foundation is turned to the aggregative weighted related coefficient maximum, optimal color sample combination, and the gamut volume with the combined sample under light conditions to be measured are determined, as final index.
Description
Technical field
The invention belongs to lighting quality assessment technique fields, and in particular to a kind of illumination hobby based on color card optimization
Spend evaluation index construction method and system.
Background technique
Lighting quality evaluation is of great significance for the development of lighting industry, scientific and reasonable lighting quality evaluation side
Method is the prerequisite for instructing illuminating product design and production.Currently, with the continuous development of lighting engineering, industry 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 hot spot of industry research in recent years, because it is more comprehensive complete
Face and consumer intuitively is reflected to the fancy grade of light source product.Currently, in terms of illumination preference degree evaluation index building,
Most researchers are by specific experiment (certain observation object, specific user group, specific light source product), to obtain
Qualitative or quantitative related conclusion out.
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:Szab ó F, K é ri R, Schanda J, et al.A study of preferred colour
rendering of light sources:Home lighting.Lighting Research&Technology 2016；
48:103-125
However, being related to light measurement, colorimetry and cognition since illumination hobby is extremely complex visual perception process
The numerous areas such as science, therefore the hobby evaluation procedure is influenced by many factors, such as observer group, observation object.Herein
In the case of kind, single research research conclusion obtained often has one-sidedness, although that is, it can be very good to explain that itself grinds
Study carefully experimental data, but for other correlative study data, then it can not reasonable dismissal.
For this purpose, being dedicated to the related number by the multinomial research work of synthesis in research application field, existing research person at present
According to more comprehensively test and assess 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 is not possible to complete novel illumination preference degree evaluation according to collected multinomial data and refer to
Target construction work.In view of the above problems, academic circles at present not yet proposes corresponding solution to industry.
Summary of the invention
The purpose of the present invention is to solve problems described in background technique, propose a kind of illumination hobby of more universality
Spend evaluation index construction method and system.
The technical scheme is that providing a kind of illumination preference degree evaluation index building side based on color card optimization
Method, comprising 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, it under D50/2 chrominance requirements, seeks each sample in step 1 sample set and corresponds to dominant wavelength information, and with master
Wavelength is that foundation is classified as M class；
Step 3, according to color mutability sex index, the sample that color fastness is poor in above-mentioned sample is rejected, i.e. rejecting color is easy
Degenerative index is unsatisfactory for the sample of CMCCON02 < 8；
Step 4, under D50/2 chrominance requirements, using excitation purity as foundation, it is maximum to seek excitation purity in all kinds of color samples
Sample forms final full gamut sample set G, and M sample is wherein contained in G；
Step 5, it seeks the M sample from full gamut sample set G and appoints the whole combinations for taking N number of sample, wherein N < M；
Step 6, representative illumination preference degree data S is collected, light source preference degree evaluation ordinal number P is specifically included
Group, every group includes several light source preference degrees evaluation ordinal number and each corresponding light source relative spectral power distributions information, the light source
Preference degree evaluation ordinal number is arranged according to ascending order；
Step 7, appoint for M sample in slave full gamut sample set G striked in step 5 and take all of N number of sample
Combined situation is sought under whole combination conditions, N number of sample is each in conjunction with light source relative spectral power distributions information each in step 6
In the gamut volume of CIELAB color space under light source lighting condition；
Step 8, for N number of sample in each group of data and step 5 in S group research work collected in step 6
Whole combined situations, calculate the P group light source preference degree mentioned in step 6 and evaluate each light striked by ordinal number and step 7
SPEARMAN correlation coefficient r between the gamut volume of source；
Step 9, it is combined for whole color samples in step 5, solves the aggregative weighted phase relation based on meta-analysis
Number R；
Step 10, principle is turned to the aggregative weighted coefficient R maximum that step 9 solves, determines optimal color sample group
Close O；
Step 11, it for light source arbitrarily to be tested and assessed, for its relative spectral power distributions, seeks determining it in step 10
Color sample combines O in the gamut volume of CIELAB color space, and as final light shines preference degree evaluation index magnitude.
Moreover, in step 2 sample set classification quantity M value value range for 18≤M≤20, with dominant wavelength to data set into
All samples that dominant wavelength is negative value are divided into 3 groups when row grouping, are later flat according to carrying out with dominant wavelength by other samples
It is grouped, is divided into M-3 group；N value value range is 14≤N≤16 and N < M-3 in step 5.
Moreover, representative studies quantity S value range is S > 6 in step 6.
Moreover, using algorithm of convex hull or α-in the method that CIELAB color space solves colour gamut in step 7 and step 11
Shape algorithm.
Moreover, the formula for solving aggregative weighted coefficient R using meta-analysis in step 9 is as follows,
Wherein, P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiIt is evaluated for i-th group of light source preference degree
The product of light source type and observer's number employed in ordinal number is obtained, r by step 6)iIt is evaluated for i-th group of light source preference degree
SPEARMAN related coefficient between ordinal number and each light source gamut volume.
The present invention provides a kind of illumination preference degree evaluation index building system based on color card optimization, including with lower die
Block:
Typical color sample spectrum data set constructs 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 categorization module, under D50/2 chrominance requirements, seeking typical color sample spectrum data set building mould
Each sample corresponds to dominant wavelength information in block sample set, and is that foundation is classified as M class with dominant wavelength；
Color fastness optimization module, for rejecting the sample that color fastness is poor in above-mentioned sample according to color mutability sex index,
Reject the sample that color mutability sex index is unsatisfactory for CMCCON02 < 8；
Full gamut sample set constructs module, for using excitation purity as foundation, seeking all kinds of colors under D50/2 chrominance requirements
The maximum sample of excitation purity in color sample forms final full gamut sample set G, and M sample is wherein contained in G；
Sample group closes computing module, and the whole of N number of sample is taken for seeking the M sample from full gamut sample set G times
It combines, wherein N < M；
Correlative study data collection module specifically includes light for collecting representative illumination preference degree data S
Source preference degree evaluates ordinal number P group, and every group includes several light source preference degrees evaluation ordinal number and each corresponding light source relative spectral power
Distributed intelligence；
Gamut volume computing module, for being closed in slave full gamut sample set G striked in computing module for sample group
M sample appoint and take all combined situations of N number of sample, in conjunction with light source relative spectral function each in correlative study data collection module
Rate distributed intelligence is sought under whole combination conditions, and N number of sample is under each light source lighting condition in the color of CIELAB color space
Domain volume；
Related coefficient computing module, for in collected S research work 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 source preference degree evaluation ordinal number and gamut volume computing module mentioned in module
Between SPEARMAN correlation coefficient r；
Aggregative weighted related coefficient computing module, for closing whole color sample groups in computing module for sample group
It closes, solves the aggregative weighted coefficient R based on meta-analysis；
Optimal color sample selecting module, the aggregative weighted for being solved with aggregative weighted related coefficient computing module are related
Coefficients R maximum turns to principle, determines optimal color sample combination O；
Gamut volume computing module seeks optimal color for its relative spectral power distributions for light source arbitrarily to be tested and assessed
The color sample combination O determined in color sample selection module shines in the gamut volume of CIELAB color space, as final light
Preference degree evaluation index magnitude.
Moreover, sample set classification quantity M value value range is 18≤M≤20 in dominant wavelength categorization module, with dominant wavelength
All samples that dominant wavelength is negative value are divided into 3 groups when being grouped to data set, are with dominant wavelength by other samples later
According to average packet is carried out, it is divided into M-3 group；It is 14≤N≤16 and N < M- that sample group, which closes N value value range in computing module,
3。
Moreover, representative studies quantity S value range is S > 6 in correlative study data collection module.
Moreover, solving color in CIELAB color space in gamut volume computing module and optimal color sample selecting module
The method of domain volume uses algorithm of convex hull or α-shape algorithm.
Moreover, the public affairs of aggregative weighted coefficient R are solved in aggregative weighted related coefficient computing module using meta-analysis
Formula is as follows,
Wherein P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiIt is evaluated for i-th group of light source preference degree
The product of light source type and observer's number employed in ordinal number, is obtained, r by correlative study data collection moduleiIt is i-th group
Light source preference degree evaluates the SPEARMAN related coefficient 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 based on color card optimization proposed by the present invention, with color
Color sample optimization is technological approaches, is quantified as construction method with sample gamut volume, effective by the meta-analysis of related coefficient
Combine the research achievement in existing field, to ensure that the comprehensive and robustness of constructed index.The method is more managed
That thinks solves background technology part 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.Since technical solution of the present invention has important application meaning, by
Plan to multiple project supports: 1. project of national nature science fund project, 61505149, the 2. Wuhan City youth morning twilight talent
2016070204010111,3. Hubei Province Nsfc Projects 2015CFB204,4 Shenzhen's basic research projects
JCYJ20150422150029093.Technical solution of the present invention is protected, China's relevant industries will be competed leading in the worldly
Position is of great significance.
Detailed description of the invention
Fig. 1 is the flow chart of the embodiment of the present invention.
Fig. 2 is the 14 optimal color sample spectral reflectivity figures finally chosen in the embodiment of the present invention.
Specific embodiment
In conjunction with attached drawing, the embodiment of the present invention is provided and is described in detail below.
A kind of illumination preference degree evaluation index constructing technology based on color card optimization that embodiment as shown in Figure 1 provides
Scheme is optimized for technological approaches with color sample, is quantified as construction method with sample gamut volume, passes through assembling for related coefficient
Analysis effectively combines the research achievement in existing field, to ensure that the comprehensive and robustness of constructed index.This side
Method it is ideal solve background technology part 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 constructs typical color sample set using 8560 color samples, is to grind with 8 groups of existing research datas
Study carefully object, construct 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), total 16 kinds of Memory Colour Rendering Index (MCRI) etc. existing classical index preference degrees are pre-
Precision is surveyed to be compared.It since bibliography is more, does 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 to light source, object involved by the studies above and observations
Person group constructs the preference degree index of other light scenes, and this method is equally applicable.
Technical solution of the present invention can be realized by those skilled in the art using computer software technology automatic when being embodied
Operation.Embodiment provide method flow the following steps are included:
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；
Typical case's color sample spectrum reflectivity data collection that embodiment uses the prior art to announce, it includes be uniformly distributed
Full gamut sample colo(u)r atlas 8560, wave-length coverage 400nm-700nm.It is detailed in 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) it under D50/2 chrominance requirements, seeks each sample in 1) sample set and corresponds to dominant wavelength information, and on this basis will
It is divided into M class；Moreover, sample set classification quantity M value value range is 18≤M≤20, data set is divided with dominant wavelength
All samples that dominant wavelength is negative value are divided into 3 groups when group, are later according to progress average mark with dominant wavelength by other samples
Group；It is divided into M-3 group.
In embodiment, M value is 18, i.e., all samples that dominant wavelength is negative value is divided into 3 groups, later by other samples
This is that foundation carries out respectively 15 groups with dominant wavelength.Wherein, dominant wavelength packet node is respectively as follows: -505nm in embodiment, -
553nm,-569nm,0nm,468nm,478nm,485nm,491nm,499nm,518nm,549nm,565nm,573nm,579nm,
585nm,592nm,601nm,612nm.Wherein, dominant wavelength calculation method 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 color mutability sex index (CMCCON02 < 8)；Reject the sample that color fastness is poor in above-mentioned sample；
In embodiment, the color sample for being unsatisfactory for (CMCCON02 < 8) condition is rejected in 8560 samples from 1) altogether
This 1054.Wherein CMCCON02 index is calculated as the 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 purity as foundation, the maximum sample of excitation purity in all kinds of color samples is sought
This, forms final full gamut sample set G, and M sample is wherein contained in G；
In embodiment, using excitation purity as foundation, full gamut sample set G is ultimately generated, 18 samples are wherein contained in G.
Wherein, excitation purity calculation method can be found in J.Schanda.CIE colorimetry.Wiley Online Library, and 2007,
It will not go into details by the present invention.
5) it seeks the M sample from full gamut sample set G and appoints the whole combinations (N < M) for taking N number of sample, moreover, N value
Value range is 14≤N≤16 and N < M-3.
In embodiment, M value 18, N value 14, therefore appoint the whole combinations for taking N number of sample shared from M sample herein
3060 kinds.
6) representative illumination preference degree data S is collected, specifically includes light source preference degree evaluation ordinal number P group, often
Group includes several light source preference degrees evaluation ordinal number (ascending order arrangement, it is big that preference degree evaluates high person's numerical sequence) and each corresponding light source phase
To spectral power distribution information；Moreover, S value range is S > 6.
In embodiment, amount to representative illumination preference degree data S=8 of collection, because that may wrap in each research
Containing multiple illumination evaluation scenes (such as art work scene, fruit and vegetable scene), therefore amounts to and obtain light source preference degree evaluation sequence
Number P=32 groups, the light source type and observer's number etc. for being collected simultaneously every group of use in P group light source preference degree evaluation ordinal number are believed
Breath.Wherein, 8 researchs used by embodiment 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, can also be directly from correlative study paper
It extracts and obtains.
7) M sample being directed in slave full gamut sample set G striked in 5) appoints all combination feelings for taking N number of sample
Condition is sought under whole combination conditions, N number of sample is illuminated in each light source in conjunction with each light source relative spectral power distributions information in 6)
Under the conditions of in the gamut volume of CIELAB color space, 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, share 3060 kinds of combinations.Then for 6) Zhong Geguang
Source relative spectral power distributions information can acquire 3060 kinds of gamut volumes altogether.Wherein, CIELAB color space, algorithm of convex hull and
α-shape algorithm is the prior art, and it will not go into details herein.
8) for each group of data in collected S research work in 6) and 5) in the whole of N number of sample combine
Situation, calculate 6) mentioned in P group light source preference degree evaluate ordinal number and 7) striked by each light source gamut volume between
SPEARMAN correlation coefficient r；
In embodiment, any one for 3060 kinds of samples combination described in 5), all can be according to convex closure described in 7)
Algorithm or α-shape algorithm (embodiment specifically uses algorithm of convex hull), calculate 6) mentioned in each light source corresponding to colour gamut
Volume.Then, it for the P group light source preference degree evaluation ordinal number and corresponding light source gamut volume referred in 6), can be calculated
Corresponding SPEARMAN correlation coefficient r.In the process, since light source gamut volume has 3060 kinds, light source preference degree evaluates ordinal number
There is P=32 group, so step calculates 3060*32=97920 group SPEARMAN correlation coefficient r.
9) the whole color samples combination being directed in 5), solves the aggregative weighted coefficient R based on meta-analysis, formula
As follows, wherein P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source type and observer's number product, pass through step 6) obtain, riOrdinal number is evaluated for i-th group of light source preference degree
With the SPEARMAN related coefficient between each light source gamut volume.
In embodiment, this step is that 3060*32=97920 group SPEARMAN correlation coefficient r is public by above-mentioned weighting
Formula, comprehensive is 3060 groups of aggregative weighted coefficient Rs, wherein each group of R all corresponds to one group of sample combination in 5).
10) principle is turned to the aggregative weighted coefficient R maximum 9) solved, determines optimal color sample combination O；
In embodiment, the maximum value (R=0.83) of required 3060 groups of aggregative weighted coefficient Rs in 9) is chosen, it is right
Answering 14 color samples is optimal color sample combination O, and the spectral reflectivity curve of 14 color samples is as shown in Figure 2.
11) for light source arbitrarily to be tested and assessed, for its relative spectral power distributions, the color sample determined in 10) is sought
The gamut volume of this combination O, as final light shine preference degree evaluation index magnitude.
Embodiment is by taking certain fluorescent light as an example, under this light source by 14 groups of optimization samples determined by seeking in 10)
In the gamut volume of CIELAB color space, its final light is obtained according to preference degree evaluation index magnitude: 14.92.Preference degree and face
The saturation degree of color has relationship, and gamut volume is to indicate the efficiency index of color saturation, therefore, can use gamut volume as
Illumination preference degree evaluation index magnitude, in general, gamut volume is bigger, indicates that color is more saturated, and the evaluation of illumination preference degree refers to
Scalar value is higher, wherein it is the basic common sense of coloration theory that gamut volume, which solves correlation technique, and it will not go into details by the present invention.
Further to confirm the method for the invention possessed technical advantage 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
Total 16 kinds of Rendering Index (MCRI) etc. existing classical indexs, according to 8) 9) described in calculating aggregative weighted phase relation
The method of number R calculates 16 kinds of existing classical indexs for the preference degree precision of prediction of 8 research used by 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 the relevant technologies thin
Section).The results show that aggregative weighted coefficient R maximum value obtained by this 16 kinds classical indexs is R (GAI)=0.54, far below this
The invention method R=0.83.
The present invention also provides a kind of illumination preference degree evaluation indexes based on color card optimization to construct system, including following
Module:
Typical color sample spectrum data set constructs 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 categorization module, under D50/2 chrominance requirements, seeking typical color sample spectrum data set building mould
Each sample corresponds to dominant wavelength information in block sample set, and is classified as M class on this basis；
Color fastness optimization module, for according to color mutability sex index (CMCCON02 < 8)；Reject color jail in above-mentioned sample
Spend poor sample；
Full gamut sample set constructs module, for using excitation purity as foundation, seeking all kinds of colors under D50/2 chrominance requirements
The maximum sample of excitation purity in color sample forms final full gamut sample set G, and M sample is wherein contained in G；
Sample group closes computing module, and the whole of N number of sample is taken for seeking the M sample from full gamut sample set G times
It combines (N < M)；
Correlative study data collection module specifically includes light for collecting representative illumination preference degree data S
Source preference degree evaluates ordinal number P group, and every group comprising several light source preference degrees evaluation ordinal number, (ascending order arrangement, preference degree evaluate Gao Zhexu
Numerical value is big) and each corresponding light source relative spectral power distributions information；
Gamut volume computing module, for being closed in slave full gamut sample set G striked in computing module for sample group
M sample appoint and take all combined situations of N number of sample, in conjunction with light source relative spectral function each in correlative study data collection module
Rate distributed intelligence is sought under whole combination conditions, and N number of sample is under each light source lighting condition in the colour gamut of CIELAB color space
Volume；
Related coefficient computing module, for in collected S group research work 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
Mentioned in module light source preference degree evaluation ordinal number and gamut volume computing module striked by each light source gamut volume it
Between SPEARMAN correlation coefficient r；
Aggregative weighted related coefficient computing module, for closing whole color sample groups in computing module for sample group
It closes, solves the aggregative weighted coefficient R based on meta-analysis；
Optimal color sample selecting module, the aggregative weighted for being solved with aggregative weighted related coefficient computing module are related
Coefficients R maximum turns to principle, determines optimal color sample combination O；
Gamut volume computing module seeks optimal color for its relative spectral power distributions for light source arbitrarily to be tested and assessed
The color sample combination O determined in color sample selection module shines in the gamut volume of CIELAB color space, as final light
Preference degree evaluation index magnitude.
Wherein, sample set classification quantity M value value range is 18≤M≤20 in dominant wavelength categorization module.With dominant wavelength
All samples that dominant wavelength is negative value are divided into 3 groups when being grouped to data set, are with dominant wavelength by other samples later
According to progress average packet；It is divided into M-3 group.
Wherein, it is 14≤N≤16 and N < M-3 that sample group, which closes N value value range in computing module,.
Wherein, representative studies quantity S value range is 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 uses
Algorithm of convex hull or α-shape algorithm.
Wherein, the public affairs of aggregative weighted coefficient R are solved in aggregative weighted related coefficient computing module using meta-analysis
Formula is as follows, and wherein P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiSequence is evaluated for i-th group of light source preference degree
The product of light source type and observer's number employed in number, is obtained, r by correlative study data collection moduleiFor i-th group of light
Source preference degree evaluates the SPEARMAN related coefficient between ordinal number and each light source gamut volume.
Each module specific implementation is corresponding with each step, and it will not go into details by the present invention.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (10)
1. a kind of illumination preference degree evaluation index construction method based on color card optimization, which is characterized in that including following step
It is rapid:
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, it under D50/2 chrominance requirements, seeks each sample in step 1 sample set and corresponds to dominant wavelength information, and with dominant wavelength
M class is classified as foundation；
Step 3, according to color mutability sex index, the sample that color fastness is poor in above-mentioned sample is rejected, i.e. rejecting color mutability
Index is unsatisfactory for the sample of CMCCON02 < 8；
Step 4, it under D50/2 chrominance requirements, using excitation purity as foundation, seeks through color in step 1-3 treated color sample
The maximum sample of purity forms final full gamut sample set G, and M sample is wherein contained in G；
Step 5, it seeks the M sample from full gamut sample set G and appoints the whole combinations for taking N number of sample, wherein N < M；
Step 6, representative illumination preference degree data S is collected, specifically includes light source preference degree evaluation ordinal number P group, often
Include several light source preference degrees evaluation ordinal number and each corresponding light source relative spectral power distributions information in group；
Step 7, all combinations for taking N number of sample are appointed for M sample in slave full gamut sample set G striked in step 5
Situation is sought under whole combination conditions, N number of sample is in each light source in conjunction with light source relative spectral power distributions information each in step 6
In the gamut volume of CIELAB color space under lighting condition；
Step 8, for the complete of N number of sample in each group of data and step 5 in S research work collected in step 6
Portion's combined situation calculates each light source colour striked by P group light source preference degree evaluation ordinal number and the step 7 mentioned in step 6
SPEARMAN correlation coefficient r between the volume of domain；
Step 9, it is combined for whole color samples in step 5, solves the aggregative weighted coefficient R based on meta-analysis；
Step 10, principle is turned to the aggregative weighted coefficient R maximum that step 9 solves, determines optimal color sample combination O；
Step 11, for light source arbitrarily to be tested and assessed, for its relative spectral power distributions, the color determined in step 10 is sought
Sample combines O in the gamut volume of CIELAB color space, and as final light shines preference degree evaluation index magnitude.
2. a kind of illumination preference degree evaluation index construction method based on color card optimization according to claim 1,
It is characterized in that:
Sample set classification quantity M value value range is 18≤M≤20 in step 2, when being grouped with dominant wavelength to data set
All samples that dominant wavelength is negative value are divided into 3 groups, are later to divide according to average packet is carried out with dominant wavelength by other samples
For M-3 group；N value value range is 14≤N≤16 and N < M-3 in step 5.
3. a kind of illumination preference degree evaluation index construction method based on color card optimization according to claim 1 or 2,
It is characterized by: representative studies quantity S value range is S > 6 in step 6.
4. a kind of illumination preference degree evaluation index construction method based on color card optimization according to claim 3,
It is characterized in that: solving the method for gamut volume in step 7 and step 11 in CIELAB color space using algorithm of convex hull or α-
Shape algorithm.
5. a kind of illumination preference degree evaluation index construction method based on color card optimization according to claim 4,
Be characterized in that: the formula for solving aggregative weighted coefficient R using meta-analysis in step 9 is as follows,
Wherein, P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source type and observer's number product, pass through step 6) obtain, riOrdinal number is evaluated for i-th group of light source preference degree
With the SPEARMAN related coefficient between each light source gamut volume.
6. a kind of illumination preference degree evaluation index based on color card optimization constructs system, which is characterized in that including with lower die
Block:
Typical color sample spectrum data set constructs module, can with the typical color sample for choosing typical color sample
Spectral reflectance data in light-exposed range constitutes typical color sample spectrum reflectivity data collection；
Dominant wavelength categorization module, under D50/2 chrominance requirements, seeking typical color sample spectrum data set building module sample
Each sample of this concentration corresponds to dominant wavelength information, and is that foundation is classified as M class with dominant wavelength；
Color fastness optimization module, for rejecting the sample that color fastness is poor in above-mentioned sample, that is, picking according to color mutability sex index
Except color mutability sex index is unsatisfactory for the sample of CMCCON02 < 8；
Full gamut sample set constructs module, for using excitation purity as foundation, seeking through above three under D50/2 chrominance requirements
The maximum sample of excitation purity in color sample after resume module forms final full gamut sample set G, and M sample is wherein contained in G
This；
Sample group closes computing module, appoints the whole for taking N number of sample to combine for seeking the M sample from full gamut sample set G,
Wherein N < M；
Correlative study data collection module specifically includes light source happiness for collecting representative illumination preference degree data S
Good degree evaluates ordinal number P group, includes several light source preference degrees evaluation ordinal number and corresponding light source relative spectral power distributions in every group
Information；
Gamut volume computing module, for closing M in slave full gamut sample set G striked in computing module for sample group
Sample appoints all combined situations for taking N number of sample, in conjunction with light source relative spectral power each in correlative study data collection module point
Cloth information, under the conditions of seeking each combination, N number of sample is under each light source lighting condition in the gamut volume of CIELAB color space；
Related coefficient computing module, for for each in collected S research work 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 source preference degree evaluation ordinal number and gamut volume computing module striked by between each light source gamut volume
SPEARMAN correlation coefficient r；
Aggregative weighted related coefficient computing module is asked for closing whole color samples combination in computing module for sample group
Aggregative weighted coefficient R of the solution based on meta-analysis；
Optimal color sample selecting module, the aggregative weighted related coefficient for being solved with aggregative weighted related coefficient computing module
R maximum turns to principle, determines optimal color sample combination O；
Gamut volume computing module seeks optimal color sample for its relative spectral power distributions for light source arbitrarily to be tested and assessed
The color sample combination O determined in this selecting module is in the gamut volume of CIELAB color space, and as final light is according to hobby
Spend evaluation index magnitude.
7. a kind of illumination preference degree evaluation index based on color card optimization according to claim 6 constructs system,
It is characterized in that:
Sample set classification quantity M value value range is 18≤M≤20 in dominant wavelength categorization module, with dominant wavelength to data set into
All samples that dominant wavelength is negative value are divided into 3 groups when row grouping, are later flat according to carrying out with dominant wavelength by other samples
It is grouped, is divided into M-3 group；
It is 14≤N≤16 and N < M-3 that sample group, which closes N value value range in computing module,.
8. a kind of illumination preference degree evaluation index based on color card optimization according to claim 6 or 7 constructs system,
It is characterized by: representative studies quantity S value range is S > 6 in correlative study data collection module.
9. a kind of illumination preference degree evaluation index based on color card optimization according to claim 8 constructs system,
It is characterized in that: solving colour gamut body in CIELAB color space in gamut volume computing module and optimal color sample selecting module
Long-pending method uses algorithm of convex hull or α-shape algorithm.
10. a kind of illumination preference degree evaluation index based on color card optimization according to claim 9 constructs system,
It is characterized in that: solving the formula of aggregative weighted coefficient R such as using meta-analysis in aggregative weighted related coefficient computing module
Under,
Wherein, P is the group number that contained light source preference degree evaluates ordinal number in S researchs, TiOrdinal number is evaluated for i-th group of light source preference degree
Employed in light source type and observer's number product, obtained by correlative study data collection module, riFor i-th group of light source
Preference degree evaluates the SPEARMAN related coefficient between ordinal number and each light source gamut volume.
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