With the rapid development and widespread application of Virtual Reality (VR) and Augmented Reality (AR) technologies, microdisplay technologies used in AR/VR applications have garnered significant attention from both the industrial and academic communities. Many of these displays are modulated digitally using pulse width modulation (PWM) methods. Examples include micro-LED, Liquid Crystal on Silicon (LCOS), and Digital Micromirror Device (DMD) display. In HMD applications, due to the frequent and rapid movements of the eyeball during device usage, the retina and screen exhibit corresponding displacement. The temporal light emission profile, i.e., the PWM scheme, could result in various visual artifacts, such as motion blur, flicker, double image, color breakup, dynamic false contour, and so on. These visual artifacts significantly impact the user experience and need to be addressed. In this paper, we analyzed the root cause of these artifacts by space-time diagram method, using Magic Leap 2 display as an example. We also proposed corresponding mitigation approach for each artifact to enhance the application of various micro-display technologies in AR/VR devices, thereby providing an improved user experience.
This paper proposes a no-reference image quality evaluation model for accurately assessing the quality of real-world images displayed on head-mounted display (HMD) devices. The proposed model employs a simulation of human visual system, providing a reliable measure of image quality. Initially, an efficient convolutional neural network (CNN), specifically designed for noise characteristics, is utilized to obtain a near-perfectly noise-reduced image. The difference between this image and the target image is then calculated in the linear domain. To emulate the contrast sensitivity and masking effects inherent in the human visual system, we introduce a sophisticated frequency-domain filter model in a uniform color space. The resulting multidimensional data from the filters are aggregated and corrected based on the average brightness. Our model's performance is validated against Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics using the TID2013 dataset, revealing superior correlation coefficients. Human factors experiments further confirm the model's reliability and practicality in real-world scenarios.
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