CQS色质指数：LED光源色彩评价新标准

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“Color Quality Scale（CQS）是一种用于评估光源色彩质量的指标，由Wendy Davis和Yoshi Ohno在国家 Institute of Standards and Technology（NIST）开发。CQS旨在解决传统色彩显色指数（CRI）在评估LED白光源时存在的不足，并考虑更广泛的色彩品质维度。” 在照明领域，色彩质量是评价光源性能的关键因素之一。传统的色彩显色指数（Color Rendering Index，简称CRI）虽然广泛使用，但在评估基于白色发光二极管（White Light Emitting Diodes, LED）的光源时暴露出局限性。CQS（Color Quality Scale）正是为了解决这些问题而设计的，它扩大了对色彩质量评价的范围。 CQS和CRI之间存在许多相似之处，但也有关键的不同。两者都是基于测试样本的方法，通过比较在测试光源照射下一组反射样本的颜色表现与参考光源下的颜色表现来评估光源。然而，CQS在实施过程中进行了改进： 1. **样本选择**：CQS采用了更大规模的高纯度反射样本集，这使得它能够更全面地反映光源对不同颜色的影响。 2. **色彩差异计算**：CQS使用根均方（Root Mean Square, RMS）方法结合这些样本的色彩差异，这种方法可以更准确地捕捉到光源对色彩再现的细微变化。 3. **色度处理**：与CRI不同，CQS不会因为光源导致物体颜色的饱和度增加而进行惩罚，但是，如果光源导致颜色失真或降低饱和度，CQS会进行相应的扣分。 4. **全面性**：CQS的设计旨在包含更多色彩品质的维度，如颜色的鲜艳度、自然度以及视觉舒适度等，使得评价更加全面。 CQS的引入对于LED照明以及其他新型光源的评价提供了更为科学和客观的标准。在选择照明设备时，除了考虑能源效率和寿命，CQS成为了衡量光源色彩质量的重要参考。设计师、工程师和消费者可以根据CQS的评分来判断光源是否能够满足对颜色真实性和视觉效果的高要求。通过CQS，我们可以更好地理解和优化光源在实际应用中的色彩表现，从而提升整体的视觉体验。

more saturated under higher illuminances 共Hunt effect兲.

Therefore, if an artiﬁcial light source increases object satu-

ration 共relative to the reference illuminant兲, the object may

actually appear more like it would when illuminated by real

daylight. This may make the object actually appear more

natural to observers.

The ability to distinguish between similar colors, chro-

matic discrimination is another dimension of color quality

that can deviate from absolute ﬁdelity. The number of ob-

ject colors that a light source permits discrimination be-

tween can be inferred by the gamut area 共of rendered object

colors兲 of the light source. For instance, if one selects a set

of reﬂective samples and plots them in CIELAB with dif-

ferent light sources as the illuminants, the spacing between

samples will be larger for some light sources, resulting in

larger gamut areas, than others. When the distance between

samples is larger in a uniform color space, the samples

appear more different from each other 共than when distances

are smaller兲 and an observer would be able to distinguish a

greater number of colors intermediate to the two samples.

In addition to increased chromatic discrimination, larger

object gamut areas have been associated with increased

perceived brightness, enhanced visual clarity, and increased

object color saturation.

17,18

Gamut area is clearly a useful

measure for certain color-quality properties of light sources

and has been proposed as the central component to a num-

ber of proposed color-rendering metrics.

7,19–21

Finally, it was decided a priori that the new metric

would yield a one-number output between zero and 100.

The CRI can generate outputs with large negative numbers

for very poor test sources. For instance, for a low-pressure

sodium lamp, R

=−47. Color rendering is virtually nonex-

istent with this lamp. A score of zero would effectively

communicate the same message. Negative values simply do

not convey any useful information and have the potential to

confuse users.

The decision to restrict the output of the new metric to

one number is certainly controversial. The argument has

been made that it is impossible to communicate the differ-

ent dimensions of quality with only one number.

22,23

In-

deed, in some cases different dimensions of color quality,

such as ﬁdelity and preference, can be contradictory. A met-

ric to assess a property like color quality inherently con-

denses information. After all, if the goal was to provide all

possible information about how a given light source would

render object colors, then one could use the spectral power

distribution of the source and colorimetric formulae to de-

termine the detailed color-rendering information 共e.g., di-

rection and magnitude of hue, chroma, and lightness shifts兲

of countless object colors. Even with all that information,

most users would still need guidance in how to use the

information to judge the suitability of a light source for a

speciﬁc application. The purpose of a metric is to condense

such an immense amount of information into something

manageable and useful. In order to be useful for the great-

est number of users, most of whom have very limited

knowledge of colorimetry, a one-number output is desir-

able. Though most users will not know exactly how the

number was determined or precisely what it means, this is

readily accepted by a majority of people. Throughout the

course of our lives, we use many measurement scales,

whose precise meanings and measurement methodologies

are unknown to us, without concern. Examples of such

measurement scales include shoe sizes, octane ratings of

gasoline, and radio station frequencies. Though most

people do not know precisely how those numbers are de-

termined, they ﬁnd the scales useful and have a general

understanding of how different outputs relate to each other

共a larger shoe size means a bigger foot兲. However, it was

acknowledged that additional outputs, for expert users

needing specialized information, would be useful and

should be created to supplement the one-number general

output.

3 CQS

Led by these guiding principles, a method for the evalua-

tion of light source color quality was developed through

computational analyses and colorimetric simulations. The

resulting metric was named the CQS, a clear nod to the CRI

but sufﬁciently different to avoid confusion among users. A

thorough account of the calculations involved in the CQS is

provided here. Readers who are knowledgeable in basic

colorimetry may ﬁnd the level of detail to be excessive, but

it was deemed important to provide complete enough infor-

mation that even a colorimetry novice could carry out the

calculations. A spreadsheet, with all of the calculations

implemented as well as additional features, such as the dis-

play of simulated sample colors, is also available from the

authors.

3.1 Reference Illuminant

The CQS, like the CRI, is a test-sample method. That is,

color differences 共in a uniform object color space兲 are cal-

culated for a predetermined set of reﬂective samples when

illuminated by a test source and a reference illuminant. In

essence, through a simulation, the appearance of the object

colors is determined and compared when illuminated by the

test source and the reference illuminant. The reference illu-

minants are the same as those used by the CRI. For test

sources of ⬍5000 K, the reference illuminant is a Planck-

ian radiator at the same CCT as the test source. These cal-

culation procedures are given in CIE’s primary colorimetry

publication

but are repeated below. The spectrum of the

Planckian reference illuminant, S

ref

共␭兲, is calculated by

ref

共␭,T兲 =

e,␭

共␭,T兲

e,␭

共560 nm,T兲

, 共3兲

where T is the CCT of the test source and L

e,␭

is the relative

spectral radiance calculated by

e,␭

共␭,T兲 = ␭

−5

冋

exp

冉

1.4388 ⫻ 10

−2

␭T

冊

−1

册

−1

. 共4兲

For test sources at 艌5000 K, the reference is a phase of

CIE Daylight illuminant having the same CCT as the test

source. The method for calculating the spectral power dis-

tribution of the daylight illuminant begins with determining

the chromaticity coordinates 共x

兲 of the illuminant. For

illuminants up to and including 7000 K, x

Davis and Ohno: Color quality scale

Optical Engineering March 2010/Vol. 49共3兲033602-4

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