Effects of chromatic image statistics on illumination induced color differences

Abstract

We measure the color fidelity of visual scenes that are rendered under different (simulated) illuminants and shown on a calibrated LCD display. Observers make triad illuminant comparisons involving the renderings from two chromatic test illuminants and one achromatic reference illuminant shown simultaneously. Four chromatic test illuminants are used: two along the daylight locus (yellow and blue), and two perpendicular to it (red and green). The observers select the rendering having the best color fidelity, thereby indirectly judging which of the two test illuminants induces the smallest color differences compared to the reference. Both multicolor test scenes and natural scenes are studied. The multicolor scenes are synthesized and represent ellipsoidal distributions in CIELAB chromaticity space having the same mean chromaticity but different chromatic orientations. We show that, for those distributions, color fidelity is best when the vector of the illuminant change (pointing from neutral to chromatic) is parallel to the major axis of the scene's chromatic distribution. For our selection of natural scenes, which generally have much broader chromatic distributions, we measure a higher color fidelity for the yellow and blue illuminants than for red and green. Scrambled versions of the natural images are also studied to exclude possible semantic effects. We quantitatively predict the average observer response (i.e., the illuminant probability) with four types of models, differing in the extent to which they incorporate information processing by the visual system. Results show different levels of performance for the models, and different levels for the multicolor scenes and the natural scenes. Overall, models based on the scene averaged color difference have the best performance. We discuss how color constancy algorithms may be improved by exploiting knowledge of the chromatic distribution of the visual scene.

Illumination discrimination for chromatically biased illuminations: Implications for color constancy

“…The illumination discrimination task (IDT) that we employ was introduced by Pearce, Crichton, Mackiewicz, Finlayson, and Hurlbert (2014). A number of other recent studies also employ similar methods (Alvaro, Linhares, Moreira, Lillo, & Nascimento, 2017; Radonjić, Pearce et al, 2016; Radonjić et al, 2018; Weiss, Witzel, & Gegenfurtner, 2017; see also Lucassen, Gevers, Giksenij, & Dekker, 2013). On each trial of the IDT, the participant views three successively presented scenes: a reference scene and then two comparisons.…”

Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution

“…These statistics were assumed to provide insight into where the true achromatic values in the scene were and thus insight into the illuminant direction [2,5,19]. Statistical approaches are still popular to date [20][21][22][23] given their efficiency. In addition to examining the RGB colors directly, spatio-statistical methods were developed (e.g., [3,21,[24][25][26][27][28][29]) that used gradient and frequency information (image derivatives, difference of Gaussians, etc.).…”

How psychophysical methods influence optimizations of color difference formulas

“…Two-Alternative Forced Choice A 2AFC experiment requires no anchor pairs. Observers are confronted with three samples, A, B, and C. They have to choose which color difference is smaller, the one between A and B or the one between B and C. This method has been applied (under various names) in only a few studies related to color differences [18][19][20][21], but as far as we know it was never used to develop color difference formulas. Therefore, it may be less clear how the resulting visual data can be used for deriving tolerance ellipsoids.…”