Journal of the Society for Information Display最新文献

Binocular rendering in augmented reality (AR) displays is prone to several artifacts that can impact visual performance, comfort, and user experience. A number of these artifacts stem from conflicts in the visual system. Vergence–accommodation conflicts (VAC), that is, the mismatch between vergence demand (driven by the horizontal binocular disparity of the augmentation) and the accommodative demand (driven by the focal distance of the display), and their impact on visual processing have been well described. However, other conflicts such stereopsis–occlusion conflicts, stemming from the absent or erroneous occlusion of the augmentations when rendered in depth behind a physical object, remain rather underexplored. In this study, we mainly focused on the impact of stereopsis–occlusion conflicts on visual performance. The results indicate that stereopsis–occlusion conflicts are a real concern for an effortless and immersive AR experience and that stereopsis–occlusion conflicts, under certain conditions, can be more detrimental to visual performance than VAC.

The perceptual uniformity of the CIE 1931 (x, y) and CIE 1976 (u', v') chromaticity spaces is examined using the latest color difference metric. A honeycomb sampling method reveals that the u'v' space is less uniform than the xy space, highlighting the need to abandon the u'v' space and adopt gamut rings for color gamut visualization.

Augmented reality head-up display (AR-HUD) systems utilizing computer-generated hologram technology have emerged as a focal point of optical innovation. Traditional display systems often rely on arrays of projectors or multiple spatial light modulators (SLMs) for multidepth projection; thus, these systems are typically complex and expensive. To address these challenges, the present study developed a miniaturized AR-HUD system that integrates an advanced driver assistance system (ADAS) with holographic display technology. By leveraging a single SLM for spatial multiplexing, this AR-HUD system achieves a streamlined design, enhancing real-time data visualization and situational awareness. The proposed AR-HUD system has a simple architecture and relatively low costs, and it advances driving safety and convenience by dynamically adjusting the depth of holographic images through deep learning techniques.

Micro-LED is considered as the new generation of display with the long-lifetime, high contrast and brightness, splicing capability, and so forth. Mass transfer is the bottleneck that limits the manufacturing of Micro-LED display at large volume, due to factors such as laser-lift off, pick-up, and metal bonding. Metal bonding between the Micro-LED chip and the substrate is one of the most important factors to the light-up yield. To solve the key thermal mismatch issue between the backplane and Micro-LED donor during heating, the laser-assisted bonding process could be a viable solution. We present a study in metallic element Au/Sn with high productivity, capability and low cost, as well as good thermal and electrical conductivity, excellent bonding strength, and low sensitivity to surface roughness. The result is presented in a 62 × 78 pixels full-color Micro-LED display, with reliable metal electrical connection between microscale LED devices and the backplane that can be achieved under 250°C without any damage to the backplane. In conclusion, the micromorphology and formation mechanism of different alloy phase compositions of welded metal elements have been thoroughly studied, and a feasible technological methodology has been developed for the upcoming mass production of Micro-LED display modules.

This study investigates the impact of five personality traits of operators on automation complacency within human-machine collaborative systems. The aim is to provide a basis for the design of human-machine collaborative systems, thereby optimizing the systems to accommodate users with different personality traits and improving the safety and efficiency of collaboration. An experiment was conducted on the OpenMATB (Open-source Multi-Attribute Task Battery) platform, collecting data on personality traits, automation complacency scale scores, task performance metrics, and EEG (Electroencephalography) data from 48 participants. The analysis revealed that individuals with low conscientiousness, high openness, and low agreeableness are more likely to exhibit automation complacency. Conversely, individuals with low neuroticism and extraversion did not show significant differences in automation complacency.

Under-display camera (UDC) technology integrates the camera beneath the screen, enabling a full-screen display without a notch or punch-hole design. However, inevitable degradation occurs due to diffraction caused by display pixels, leading to reduced transmittance, noise, blur, and flare. These optical distortions degrade facial recognition accuracy, limiting the practicality of UDC systems. Despite ongoing research on UDC image restoration, studies on facial recognition performance with UDC remain limited. While a few studies have attempted to generate UDC-distorted images containing human faces, they overlook actual UDC degradations and the pair-matching task, a key smartphone application. This paper presents three UDC facial recognition datasets generated by a generative adversarial network (GAN) trained on synthetic and real-world datasets with flares. We aim to analyze the impact of UDC restoration on face recognition accuracy, particularly in pair matching, contributing to the expansion of UDC face recognition research.