The Color Rendering Index (CRI) is a widely used metric to describe color accuracy and fidelity. It is calculated as an average score on 8 color samples (called TCS or test color samples), with an additional 7 supplemental color samples for the extended CRI (e) metric.
Each of these scores is called Ri, where i represents the TCS number. For example, R9 is a commonly referenced deep red color score and is an important indicator of color quality for many applications.
CRI itself is a single number, and this is both a blessing and a curse. It is a great, convenient metric that is both intuitive and simple to communicate, but at the same time, particular color samples can distort the truth behind whether a light source is truly a high color rendering light source.
For most indoor and commercial lighting applications, 80 CRI (Ra) is the general baseline for acceptable color rendering. For applications where color appearance is important for the work being done inside, or can contribute to improved aesthetics, 90 CRI (Ra) and above can be a good starting point. Lights in this CRI range are generally considered high CRI lights.
Types of applications where 90 CRI (Ra) might be needed for professional reasons include hospitals, textile factories, printing facilities or paint shops. Areas where improved aesthetics could be important include high end hotels and retail stores, residences and photography studios.
The test color samples are used in the calculation of CRI by simulating the reflectance spectrum from a light source under question, and comparing it to a reference source which depends on the color temperature, but in general is a variant of black body radiation or the daylight illuminant.
The R value for a particular color indicates the ability of a light source to faithfully render that particular color. Therefore, to characterize the overall color rendering capability of a light source across a variety of colors, the CRI formula takes an average of the R values.
Below we have developed a tool that would help you plot a graph based on the R values. Just fill in the R 1-15 values and add data to the graph then your graph is ready to be saved and used where needed.
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