As one factor among others, circadian effectiveness depends on the spatial light distribution of the prevalent lighting conditions. In a typical office context focusing on computer work, the light that is experienced by the office workers is usually composed of a direct component emitted by the room luminaires and the computer monitors as well as by an indirect component reflected from the walls, surfaces, and ceiling. Due to this multi-directional light pattern, spatially resolved light measurements are required for an adequate prediction of non-visual light-induced effects. In this work, we therefore propose a novel methodological framework for spatially resolved light measurements that allows for an estimate of the circadian effectiveness of a lighting situation for variable field of view (FOV) definitions. Results of exemplary in-field office light measurements are reported and compared to those obtained from standard spectral radiometry to validate the accuracy of the proposed approach. The corresponding relative error is found to be of the order of 3–6%, which denotes an acceptable range for most practical applications. In addition, the impact of different FOVs as well as non-zero measurement angles will be investigated.

The Sternberg task is a widely used tool for assessing the working memory performance in vision and cognitive science. It is possible to apply a visual or auditory variant of the Sternberg task to query the memory load. However, previous studies have shown that the subjects’ corresponding reaction times differ dependent on the used variant. In this work, we present an experimental approach that is intended to correct the reaction time differences observed between auditory and visual item presentation. We found that the subjects’ reaction time offset is related to the encoding speed of a single probe item. After correcting for these individual encoding latencies, differences in the results of both the auditory and visual Sternberg task become non-significant, $p=0.252$. Thus, an equal task difficulty can be concluded for both variants of item presentation.

Due to their potential use as an internal reference, memory colors may provide an excellent approach for the color rendition evaluation of white light sources in terms of predicting visual appreciation. Because of certain limitations in the design of existing memory-related color quality measures, a new metric based on the outcome of a series of recently conducted memory color appearance rating experiments is proposed in this work. In order to validate its predictive performance, a meta-correlation analysis on a comprehensive set of preference rating data collected from literature is performed. Results indicate that the new metric proposal outperforms established color quality measures and is capable of correctly predicting the rank order of light sources in different lighting scenarios. The future inclusion of this new metric into a comprehensive lighting quality model may serve as a valuable tool for the lighting designer to create optimally lit environments for humans that do not only support the visual task fulfillment but also increase the users’ well-being and emotional comfort by rendering the perceived space in such a way that it complies with the people’s inherent memory references.

Provoking high user acceptance in lighting can be a very challenging task and demands suitable tools for properly modelling and predicting the users’ perception of the lit environment. Recently, a new model formalism based on the perceptually relevant attributes of perceived brightness, visual clarity, and color preference has been introduced and successfully applied in some preliminary studies. However, a proof of the model’s applicability from a lighting practitioner’s point of view for realistic lighting scenarios and use-cases is still pending and should be performed as part of this work. For this purpose, results of two dedicated lighting condition rating experiments representing different lighting contexts are reported. It is shown that the model predictions for all three perceptual attributes exhibit excellent linear correlations with the respective subject mean ratings and, in all cases, correctly predict the test light sources’ rank order. These results clearly emphasize the applicability and practical relevance of the model and confirm the suitability of its multi-dimensional approach.

Adaptive spectral tuning of solid-state lighting requires perceptually grounded models of color preference that account for adaptation. This study quantifies how adaptation illuminance at three levels modulates preferred local chroma and hue shifts in four TM-30 hue bins (1: red, 4: yellow, 6: green, 12: blue) using ecologically valid, multi-object stimuli and a dual-lighting design that allowed independent modulation of object color appearance and adaptation illumination. Pooled observer ratings from 51 participants were modeled with bin-wise bivariate Gaussian functions in local chroma-hue shift space, capturing both location and anisotropy of preference distributions. Preference maxima were estimated via bootstrapping, and model contours defined confidence ellipses. Results show a consistent logarithmic decrease in preferred chroma shift with increasing illuminance, while hue-shift trends vary by bin, accompanied by decreasing tolerance regions at higher illuminance. Compared to former studies, the findings predict preference for higher chroma levels under typical indoor scenarios and exhibit a less steep decline with increasing illuminance. The present results provide broader, adaptation-aware, bin-wise mappings suitable for predictive preference metrics and adaptive lighting optimization.

Fully digital microscopes are becoming more and more common in surgical applications. In addition to high‐resolution stereoscopic images of the operating field, which can be transmitted over long distances or stored directly, these systems offer further potentials by supporting the surgical workflow based on their fully digital image processing chain. For example, the image display can be adapted to the respective surgical scenario by adaptive color reproduction optimization or image overlays with additional information, such as the tissue topology. Knowledge of this topology can be used for computer‐assisted or augmented‐reality‐guided microsurgical treatments and enables additional features such as spatially resolved spectral reconstruction of surface reflectance. In this work, a new method for high‐resolution depth measurements in digital microsurgical applications is proposed, which is based on the principle of laser triangulation. Part of this method is a sensor data fusion procedure to properly match the laser scanner and camera data. In this context, a strategy based on radial basis function interpolation techniques is presented to handle missing or corrupt data, which, due to the measuring principle, can occur on steep edges and through occlusion. The proposed method is used for the acquisition of high‐resolution depth profiles of various organic tissue samples, proving the feasibility of the proposed concept as a supporting technology in a digital microsurgical workflow.