Designing Exterior Lighting for Safety and Comfort While Minimizing Light Pollution, Energy Consumption, and Cost

Artificial light at night brings many benefits to society. However, these benefits do not come without costs. One environmental issue that is often overlooked in the design of public spaces and infrastructure is light pollution. At the expense of large amounts of energy, light pollution causes numerous harmful effects on human health, ecosystems, and the night sky. Today, the problem is becoming more widespread, especially with the increasing use of bright, white LED luminaires. Thus, it is imperative for designers and engineers to create smarter lighting designs that not only allow for safety and comfort at night but also promote human health and environmental stewardship. This research focused on creating healthier, more sustainable outdoor lighting designs. First, the harmful effects of artificial light at night were reviewed, and general design recommendations were made for mitigating these consequences. Next, a multi-criteria decision analysis framework was developed and used to optimize illuminance and spectrum for functionality, perception, light pollution reduction, energy use, and cost. Finally, virtual reality technology was utilized to aid in adopting smarter designs that require less illumination to make public spaces feel safe and comfortable at night. The findings of this research will help lead to a more conscious use of artificial light in the future. Additional research is encouraged to further refine and develop lighting designs that promote a proper balance of human, environmental, and economic factors. With careful consideration of both the benefits and drawbacks of lighting, designers can work towards a solution to light pollution --Abstract, p. ii

The effect of display brightness and viewing distance: A dataset for visually lossless image compression

Visibility of image artifacts depends on the viewing conditions, such as display brightness and distance to the display. However, most image and video quality metrics operate under the assumption of a single standard viewing condition, without considering luminance or distance to the display. To address this limitation, we isolate brightness and distance as the components impacting the visibility of artifacts and collect a new dataset for visually lossless image compression. The dataset includes images encoded with JPEG andWebP at the quality level that makes compression artifacts imperceptible to an average observer. The visibility thresholds are collected under two luminance conditions: 10 cd/m2, simulating a dimmed mobile phone, and 220 cd/m2, which is a typical peak luminance of modern computer displays; and two distance conditions: 30 and 60 pixels per visual degree. The dataset was used to evaluate existing image quality and visibility metrics in their ability to consider display brightness and its distance to viewer. We include two deep neural network architectures, proposed to control image compression for visually lossless coding in our experiments. </jats:p

Virtual reality for smart urban lighting design: Review, applications and opportunities

More and more cities are evolving into smart cities, increasing their attractiveness, energy efficiency, and users satisfaction. Lighting systems play an important role in the evolution process, thanks to their ability to affect city life at night along with people s mood and behaviour. In this scenario, advanced lighting design methods such as virtual reality (VR) became essential to assess lighting systems from different points of view, especially those linked with the city users expectations. Initially, the review highlights a list of objective and subjective parameters to be considered for the lighting design of three main city areas/applications: roads, green areas and buildings. Besides, the state-of-Art in using VR for outdoor lighting design is established. Finally, the Unreal game engine is used to analyse the ability of VR to take into account the lighting parameters, not yet investigated in current literature and to highlight the VR potential for augmenting lighting design. The results confirm the benefit of using VR in lighting design, even if further investigations are needed to establish its reliability, especially from the photometrical point of view

Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and new membership function to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a new membership function to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images

Visibility metrics and their applications in visually lossless image compression

Visibility metrics are image metrics that predict the probability that a human observer can detect differences between a pair of images. These metrics can provide localized information in the form of visibility maps, in which each value represents a probability of detection. An important application of the visibility metric is visually lossless image compression that aims at compressing a given image to the lowest fraction of bit per pixel while keeping the compression artifacts invisible at the same time. In previous works, most visibility metrics were modeled based on largely simplified assumptions and mathematical models of human visual systems. This approach generally fits well into experimental data measured with simple stimuli, such as Gabor patches. However, it cannot predict complex non-linear effects, such as contrast masking in natural images, particularly well. To predict visibility of image differences accurately, we collected the largest visibility dataset under fixed viewing conditions for calibrating existing visibility metrics and proposed a deep neural network-based visibility metric. We demonstrated in our experiments that the deep neural network-based visibility metric significantly outperformed existing visibility metrics. However, the deep neural network-based visibility metric cannot predict visibility under varying viewing conditions, such as display brightness and viewing distances that have great impacts on the visibility of distortions. To extend the deep neural network-based visibility metric to varying viewing conditions, we collected the largest visibility dataset under varying display brightness and viewing distances. We proposed incorporating white-box modules, in other words, luminance masking and viewing distance adaptation, into the black-box deep neural network, and we found that the combination of white-box modules and black-box deep neural networks could generalize our proposed visibility metric to varying viewing conditions. To demonstrate the application of our proposed deep neural network-based visibility metric to visually lossless image compression, we collected the visually lossless image compression dataset under fixed viewing conditions and significantly improved the deep neural network-based visibility metric's accuracy of predicting visually lossless image compression threshold by pre-training the visibility metric with a synthetic dataset generated by the state-of-the-art white-box visibility metric---HDR-VDP \cite{Mantiuk2011}. In a large-scale study of 1000 images, we found that with our improved visibility metric, we can save around 60\% to 70\% bits for visually lossless image compression encoding as compared to the default visually lossless quality level of 90. Because predicting image visibility and predicting image quality are closely related research topics, we also proposed a trained perceptually uniform transform for high dynamic range images and videos quality assessments by training a perceptual encoding function on a set of subjective quality assessment datasets. We have shown that when combining the trained perceptual encoding function with standard dynamic range image quality metrics, such as peak-signal-noise-ratio (PSNR), better performance was achieved compared to the untrained version

Lighting and display screens: Models for predicting luminance limits and disturbance

An investigation of the level of disturbance caused by reflections from a variety of display screens, including interactive whiteboards, has been carried out using three test methods: Luminance adjustment, category rating and reading. The results from the luminance adjustment test and the category rating test were consistent, both showing similar significant effects of lighting-display parameters on the disturbance caused by screen reflections. In contrast, the objective measure of task performance in the reading test was barely responsive to reflections on the screens. Two models have been developed, one to predict the luminaire luminance at which 95% of observers were not disturbed by the reflections and the other to predict the rating of disturbance caused by reflections from the screens. Both models are based on lighting-display parameters including the size and luminance of the reflected light source and the specular reflectance, the effect of haze reflection and the background luminance of the display screen. These models can be used generally, to guide lighting recommendations and, specifically, to identify suitable luminaires to be used with given set of display screens or suitable display screens to be used with a given lighting installation