Re-defining the colour rendering index

1. Re-definingthe colour rendering index Peter Bodrogi Stefan Brückner Tran Quoc Khanh CIE Budapest 2009 | FG Lichttechnik | Peter Bodrogi et al.

2. New colour rendering index: our point of view • In practice, a rank order scale may be more usable than a continuous scale • It is easy for the user to understand very good, good, acceptable, not acceptable and very bad • What is the visual difference between Ra=78 and 90? And between Ra=78 and 82? • New CIECAM02-based colour spaces like CIECAM02-LCD (Luo et al, CRA, 2006) • Define the category limits for very good, good, acceptable, not acceptable and very bad in a new visual experiment in terms of CIECAM02-LCD • Computed categories for a set of test colour samples, e.g. for a new white LED light source: skin: very good; banana: good; cucumber: acceptable; etc. • Compute from the above „rating vector“ a single quantity like its median and then the new CRI like 6-median(rating vector)

3. New questionnaire Variable DEvis Variable R VariableP

4. New definition of the colour rendering index based on computed ratings 13.7 9.5 7.25 5.5

5. New definition of the colour rendering index:histograms of the computed ratings

6. New definition of the colour rendering index1: median scale (coarse) CRITU_DA_1 = (100/5) (6 - Median [{RCi} i=1…17])

7. Comparison of CRITU_DA_i with Ra (1-8) and Ra (1-14) and with CQS7.1

8. New colour rendering index: our point of view • Separate indices for the users‘ different tasks • Naturalness or colour fidelity: the true appearance of simple standalone surface colours as under „A“ or „D65“ or from long-term colour memory • Saturation enhancement can be in a separate index (preference) • CPI for preference or „flattery“; CDI: small colour differences • HRI for harmony rendering (is the appearance of a combination of colours aesthetic?) • Visual clarity, large colour differences • Acceptability of realistic scenes with many colours • Every user can select an index appropriate for the task, e.g. fidelity can be important for textile designers • Or CDI for electricians working with wires • Weighting of the indices

9. New colour rendering index: our point of viewAdditional index for inter-observer variability

10. New colour rendering index: future tasks • CATs: mostly collected under two light sources (D65 and A) • Combinations of surfaces, textures and shapes can evoke higher order neural mechanisms affecting colour constancy: e.g. memory, language, or object recognition • Compare the colour constancy index (e.g. the Brunswick ratio) with the colour rendering index • Effects of local contrast, global contrast, and image content on chromatic adaptation • Relational colour constancy: spatial cone excitation ratios in a multi-colour-surface scene remain (relatively) constant under changing illuminations but this may not hold for novel artificial light SPDs • Chromatic textures in natural scenes: the discrimination of texture stimuli is not the same as the discrimination of uniform colour patches under the test and reference illuminants • Contextual factors: higher order processing involving long term colour memory for familiar objects (e.g. human skin tone). Pictorial (hyperspectral) test images are needed instead of standalone patches.

11. New colour rendering index: future tasks The colour rendering index can be improved by advancing two lines of research: • 1. computational colour constancy • 2. colour image difference metric • Our planned experiments: • Double-chamber viewing booth experiments currently underway • Aim: 12 observers x 2 CCTs (2900K and 5000K) x 5 light sources x 20 test colour samples • Validate the scale of the new CRI_Darmstadt (very good-very bad) • Develop the inter-observer variability index • Tabletop (still life) with several coloured surfaces (textures, objects) and a test room with immersion in the visual environment (acceptability study) with several conventional and new light sources