3.3 Colour quality scale

3. Light-source properties

3.1 Colour rendering index

from the Rc set.

3.2 Colour fidelity index

to over-estimate colour performance.

colour appearance space [20].

<sup>1</sup> Defined in Section 3.5.

44

and other important, source properties.

Our purpose in this chapter is to present an approach to intelligent spectral design for any white-light source, aiming to achieve an optimum combination of

contravariant characteristics of the SPD. We next introduce a brief outline of these,

Colour rendering has been defined by the Commission Internationale de l'Éclairage, International Commission on Illumination (CIE) who have published recommendations for the method of calculation of their colour rendering index (CRI) [13] based on a knowledge of the light-source spectrum. It represents an evaluation of the average colour shift of eight defined moderate-chroma colour samples when compared under the test source and a reference source having the same correlated colour temperature (CCT).<sup>1</sup> The system includes 14 test colours in total, and the additional 6 comprise 4 highly saturated colours (red, yellow, green, and blue) plus samples representing skin and foliage colours, respectively. As of the time of writing, this is the internationally agreed method. Note that CRI and associated technology have also been covered in [14]. The two most widely quoted colour rendering terms are: Ra—the general colour rendering index, based on the colour shifts of the eight principal test colour samples; and R9—the 'special' (indi-

In our optimisation work (see Section 4.1), we also made use of several derived indices symbolised as Rb, Rc and Rmin, based, respectively, on: the 6 additional test colours; the full set of 14 test colours and the minimum individual value

Some dissatisfaction with the CIE method has arisen since the widespread adoption of LED lighting. As a consequence, the Illumination Engineering Society of North America (IES) has adopted a recommended method (TM-30-15) [15, 16] which recommends two new indices (Rf and Rg) for the classification of the colour properties of light sources. The underpinning research leading to the development of TM-30-15 [16–18] identified several weaknesses in the CIE's earlier CRI method [13], claiming that it does not adequately sample wavelength space and hence tends

The method is also based on the colour-shift concept, but now using a set of 99 test colours considered to provide uniformity of both wavelength sampling and colour-space sampling. In addition, it uses a more modern colour-difference calculation technique, CAM02UCS [19] which is a development of the basic CIECAM02

The new index Rf gives an overall assessment of colour fidelity, while gamut index Rg indicates the relative magnitudes of colour shifts for sample colours in different regions of colour space. Also available is the skin colour index, Rf skin, which is an average of two specific sample-colour indices, selected as representative of human skin. In our optimisations, we also called up the minimum value of Rf,

vidual) index for the highly saturated red colour (sample 9).

luminous efficacy and colour rendering which, as previously noted, are

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This was a precursor to TM-30-15, first proposed by Davis and Ohno of NIST (USA) [21]. The following serves as a brief introduction for the purpose of the present discussion. Again using the previously mentioned colour-shift concept, the CQS metric employs 15 saturated test colour samples, on the premise that certain light sources may render saturated colours more poorly than the de-saturated colours of the CIE's CRI method. The chromatic differences are calculated using the CIE 1976 (CIELAB) colour model [22].

The full calculation procedure has also been explained in [23]. It employs multiple steps, several of which are non-linear, resulting in the general CQS index, Q <sup>a</sup>. As with the previous two cases, the 'special CQS' (Q <sup>i</sup>) for each test colour sample may be calculated for a more thorough investigation of a test source. We have used the minimum value of Q <sup>i</sup> (designated Q min) from the set of 15 Q <sup>i</sup> values, as an optimisation parameter.

#### 3.4 Luminous efficacy of radiation

The luminous efficacy of the radiation (LER) of a light source assesses the 'lighting content' of the spectrum by comparing the visible light output (in lumens) to the total radiant output (in watts) as in Eq. 3.

$$LER = \frac{K\_m \int\_{\lambda} V(\lambda) \mathbf{S}(\lambda) d\lambda}{\int\_{\lambda} \mathbf{S}(\lambda) d\lambda} \tag{3}$$

where Km is the maximum luminous efficacy of radiation (≈683 lumen per watt), S(λ) is the spectral distribution of the light source, and V(λ) is the CIE spectral sensitivity function for human photopic vision [24]. The LER is an important determinant of the overall economy of a light source since the overall luminous efficacy is given by the product of LER with the energy conversion efficiency of the particular light source.

## 3.5 Correlated colour temperature

From the perspective of the lighting system designer, the correlated colour temperature (CCT) is the key feature in the selection of a light source since the CCT serves as an indicator of, not only the colour of the source, but also, the 'atmosphere' it will create.

The CCT is defined in [25], and its significance is that it describes the chromaticity of the source (which must be close to the Planckian locus) in the CIE (u, v) chromaticity diagram.<sup>2</sup> Note that it is possible for many different spectral power distributions (SPDs) to have the same CCT; and that CCT is not essentially linked with colour rendering, quality or fidelity.

<sup>2</sup> In CIE documentation, this is now replaced by the u<sup>0</sup> ; 2 <sup>3</sup> <sup>v</sup><sup>0</sup> � � diagram based on CIE 1976 (u<sup>0</sup> , v<sup>0</sup> ) coordinates.
