Adaptive image synthesis for compressive displays

Recent years have seen proposals for exciting new computational display technologies that are compressive in the sense that they generate high resolution images or light fields with relatively few display parameters. Image synthesis for these types of displays involves two major tasks: sampling and rendering high-dimensional target imagery, such as light fields or time-varying light fields, as well as optimizing the display parameters to provide a good approximation of the target content. In this paper, we introduce an adaptive optimization framework for compressive displays that generates high quality images and light fields using only a fraction of the total plenoptic samples. We demonstrate the framework for a large set of display technologies, including several types of auto-stereoscopic displays, high dynamic range displays, and high-resolution displays. We achieve significant performance gains, and in some cases are able to process data that would be infeasible with existing methods.University of British Columbia (UBC Four Year Doctoral Fellowship)Natural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship)United States. Defense Advanced Research Projects Agency (DARPA SCENICC program)Alfred P. Sloan Foundation (Sloan Research Fellowship)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)University of British Columbia (Dolby Research Chair at UBC

Optimizing vision and visuals: lectures on cameras, displays and perception

The evolution of the internet is underway, where immersive virtual 3D environments (commonly known as metaverse or telelife) will replace flat 2D interfaces. Crucial ingredients in this transformation are next-generation displays and cameras representing genuinely 3D visuals while meeting the human visual system's perceptual requirements. This course will provide a fast-paced introduction to optimization methods for next-generation interfaces geared towards immersive virtual 3D environments. Firstly, we will introduce lensless cameras for high dimensional compressive sensing (e.g., single exposure capture to a video or one-shot 3D). Our audience will learn to process images from a lensless camera at the end. Secondly, we introduce holographic displays as a potential candidate for next-generation displays. By the end of this course, you will learn to create your 3D images that can be viewed using a standard holographic display. Lastly, we will introduce perceptual guidance that could be an integral part of the optimization routines of displays and cameras. Our audience will gather experience in integrating perception to display and camera optimizations. This course targets a wide range of audiences, from domain experts to newcomers. To do so, examples from this course will be based on our in-house toolkit to be replicable for future use. The course material will provide example codes and a broad survey with crucial information on cameras, displays and perception

Eyeglasses-free display: towards correcting visual aberrations with computational light field displays

Millions of people worldwide need glasses or contact lenses to see or read properly. We introduce a computational display technology that predistorts the presented content for an observer, so that the target image is perceived without the need for eyewear. By designing optics in concert with prefiltering algorithms, the proposed display architecture achieves significantly higher resolution and contrast than prior approaches to vision-correcting image display. We demonstrate that inexpensive light field displays driven by efficient implementations of 4D prefiltering algorithms can produce the desired vision-corrected imagery, even for higher-order aberrations that are difficult to be corrected with glasses. The proposed computational display architecture is evaluated in simulation and with a low-cost prototype device.National Science Foundation (U.S.) (grant number IIS-1219241)National Science Foundation (U.S.) (grant number IIS-1116718)Natural Sciences and Engineering Research Council of Canada (NSERC Postdoctoral Fellowship

A Quality-Centered Analysis of Eye Tracking Data in Foveated Rendering

This work presents the analysis of data recorded by an eye tracking device in the course of evaluating a foveated rendering approach for head-mounted displays (HMDs). Foveated ren- dering methods adapt the image synthesis process to the user’s gaze and exploiting the human visual system’s limitations to increase rendering performance. Especially, foveated rendering has great potential when certain requirements have to be fulfilled, like low-latency rendering to cope with high display refresh rates. This is crucial for virtual reality (VR), as a high level of immersion, which can only be achieved with high rendering performance and also helps to reduce nausea, is an important factor in this field. We put things in context by first providing basic information about our rendering system, followed by a description of the user study and the collected data. This data stems from fixation tasks that subjects had to perform while being shown fly-through sequences of virtual scenes on an HMD. These fixation tasks consisted of a combination of various scenes and fixation modes. Besides static fixation targets, moving tar- gets on randomized paths as well as a free focus mode were tested. Using this data, we estimate the precision of the utilized eye tracker and analyze the participants’ accuracy in focusing the displayed fixation targets. Here, we also take a look at eccentricity-dependent quality ratings. Comparing this information with the users’ quality ratings given for the displayed sequences then reveals an interesting connection between fixation modes, fixation accuracy and quality ratings