Colour Calibration of a Head Mounted Display for Colour Vision Research Using Virtual Reality

Virtual reality (VR) technology offers vision researchers the opportunity to conduct immersive studies in simulated real-world scenes. However, an accurate colour calibration of the VR head mounted display (HMD), both in terms of luminance and chromaticity, is required to precisely control the presented stimuli. Such a calibration presents significant new challenges, for example, due to the large field of view of the HMD, or the software implementation used for scene rendering, which might alter the colour appearance of objects. Here, we propose a framework for calibrating an HMD using an imaging colorimeter, the I29 (Radiant Vision Systems, Redmond, WA, USA). We examine two scenarios, both with and without using a rendering software for visualisation. In addition, we present a colour constancy experiment design for VR through a gaming engine software, Unreal Engine 4. The colours of the objects of study are chosen according to the previously defined calibration. Results show a high-colour constancy performance among participants, in agreement with recent studies performed on real-world scenarios. Our studies show that our methodology allows us to control and measure the colours presented in the HMD, effectively enabling the use of VR technology for colour vision research.

Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a ‘white’ illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.

The time course of chromatic adaptation under immersive illumination

Chromatic adaptation is a major contributory mechanism to constancy, yet its extent depends on many factors - spectral, spatial and temporal - which vary between studies and hence may contribute to differences in reported constancy indices. Here, we use the achromatic adjustment method to characterise the temporal progression of chromatic adaptation under a wide range of illuminations in an immersive environment. We control both the spectral properties of the illumination at the eye and the spatial context of the adjusted surface, to disentangle global adaptation from local contrast effects. We measure the timecourse of chromatic adaptation by assessing achromatic adjustments in 6 discrete time slots over 340 seconds. We find that the change over time of the adaptation state, proximally indicated by colour constancy indices (quantified by the relative closeness of the perceptual whitepoint to the test illumination chromaticity), (a) can be modelled by a proportional rate growth function, typically requiring more than 5 minutes to stabilise; (b) depends on the contrast between the test surface and its background, specifically increasing with decreasing test-background contrast; and (c) is generally similar in both extent and rate for different test illumination chromaticities. Adaptation progression does not differ significantly between illuminations on or off the daylight locus. Our results highlight the importance of considering exposure duration and stimulus configuration, as well as the distance between the pre-adaptation (reference) and test illumination chromaticities, when using achromatic adjustment as a measure of colour constancy.

Evaluating Colour Constancy on the new MIST dataset of Multi-Illuminant Scenes

A new image test set of synthetically generated, full-spectrum images with pixelwise ground truth has been developed to aid in the evaluation of illumination estimation methods for colour constancy. The performance of 9 illumination methods is reported for this dataset along and compared to the optimal single-illuminant estimate. None of the methods specifically designed to handle multi-illuminant scenes is found to perform any better than the optimal single-illuminant case based on completely uniform illumination.