To achieve this goal we make two key observations. We focus on speeding up accommodation response.
We take a step further and ask the question of whether the capabilities of current and future display designs combined with efficient perception-inspired content optimizations can be used to improve human task performance beyond the human capabilities in the natural world. Towards Improving Eye Accommodation Performance Beyond Human Capabilities in Natural World Multi-focal plane and multi-layered light-field displays are promising solutions for addressing all visual cues observed in the real world.
#Advanced displays series#
We demonstrate that our predictor can efficiently drive the foveated rendering technique and analyze its benefits in a series of user experiments. This property is essential for the estimation of required quality before the full-resolution image is rendered. Its main feature is the ability to predict the parameters using only a low-resolution version of the current frame, even though the prediction holds for high-resolution rendering. We later use this information to derive a low-cost predictor of the foveated rendering parameters. To this end, we first study the resolution requirements at different eccentricities as a function of luminance patterns. We propose a new luminance-contrast-aware foveated rendering technique which demonstrates that the computational savings of foveated rendering can be significantly improved if local luminance contrast of the image is analyzed.
The existing rendering solutions model the sensitivity as a function of eccentricity, neglecting the fact that it also is strongly influenced by the displayed content. Image-content Aware Foveated Rendering Information on the eye fixation allows rendering quality to be reduced in peripheral vision, and the additional resources can be used to improve the quality in the foveal region. We achieve this by co-authoring textbooks, such as the one on High Dynamic Range Imaging (almost 2,300 Google Scholar citations). One of the key goals in our research vision is spreading the awareness of the importance of HVS modeling for computer graphics and image processing. The perceptual aspects of such appearance matching have not been investigated thoroughly so far, given that each printing technology might impose some unique constraints, such as strong light diffusion within the material that washes out texture details of the object. When objects are 3D printed their physical appearance is expected to match their virtual models as closely as possible. We consider 3D printing as another aspect of seamless transition between virtual and physical objects, we are aiming at developing a domain-specific perception model for faithful appearance reproduction in fabrication. Having a model of the HVS which accounts for all these factors enables dynamic reallocation of rendering resources to achieve the best visual quality. For example, the human visual system (HVS) sensitivity to brightness, contrast, and object depth may dynamically change as a function of gaze position, ambient luminance levels and accommodative state of the eye lens, which in turn might depend on the semantic scene content, object positions in screen-space, their depth levels, and observer’s motion relative to the objects. Modeling visual perception is a principled approach to optimize the sampling and reconstruction, so that the discrepancies from the perception of a reference real-world scene are minimized. Displays with such functionality require specialized rendering algorithms that sample and reconstruct important aspects of the complex light-field, which is a complete representation of the light distribution at every position and direction in a scene. To this end, a faithful reproduction of visual cues such as binocular vision, motion parallax, and eye lens accommodation is of particular importance.
Seamless blending of visual content from both of the virtual and real world by the means of augmented reality (AR) is a long-standing goal. In particular, we aim for the exploitation of perceptual effects as a means of overcoming physical limitations of display devices and enhancing the apparent image quality. Often, we refer to perceptual effects rather than physical effects, which puts more emphasis on the experience of the observers than physical measurements. This approach offers significant improvements in both the computational performance and the perceived image quality. The common goal of all research efforts of the group is the advancement of knowledge on image perception and the development of imaging algorithms with embedded computational models of the human visual system (HVS).