Gaze-contingent rendering shows promise in improving perceived quality by providing a better match between image quality and the human visual system requirements. For example, information about 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. Gaze-contingent rendering can also be used to compensate for certain limitations of display devices, such as reduced dynamic range or lack of accommodation cues. Despite this potential and the recent drop in the prices of eye trackers, the adoption of such solutions is hampered by system latency which leads to a mismatch between image quality and the actual gaze location. This is especially apparent during fast saccadic movements when the information about gaze location is significantly delayed, and the quality mismatch can be noticed. To address this problem, we suggest a new way of updating images in gaze-contingent rendering during saccades. Instead of rendering according to the current gaze position, our technique predicts where the saccade is likely to end and provides an image for the new fixation location as soon as the prediction is available. While the quality mismatch during the saccade remains unnoticed due to saccadic suppression, a correct image for the new fixation is provided before the fixation is established. This paper describes the derivation of a model for predicting saccade landing positions and demonstrates how it can be used in the context of gaze-contingent rendering to reduce the influence of system latency on the perceived quality. The technique is validated in a series of experiments for various combinations of display frame rate and eye-tracker sampling rate.

Obtaining a high quality high dynamic range (HDR) image in the presence of camera and object movement has been a long-standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: (1) how to detect ghost regions and (2) what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under-and over-exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state-of-the-art deghosting algorithms.

a) Moving people generate blending (red) and visual difference (blue) artifacts. (b) Over-smoothing gives rise to gradient inconsistency (green) artifacts.Figure 1: Our metric detects several kinds of HDR deghosting artifacts. In (a), Khan et al.'s [KAR06] output is shown in the bottom-left corner and our metric's result in the bottom-right. The same for (b), except Hu et al.'s [HGPS13] deghosting algorithm is used. Exposure sequences are shown on the top. Cyan color occurs due to both gradient and visual difference metrics producing high output. AbstractReconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.

is essential for the estimation of required quality before the full-resolution image is rendered. We demonstrate that our predictor can efficiently drive the foveated rendering technique and analyze its benefits in a series of user experiments.

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Multi-focal plane and multi-layered light-field displays are promising solutions for addressing all visual cues observed in the real world. Unfortunately, these devices usually require expensive optimizations to compute a suitable decomposition of the input light field or focal stack to drive individual display layers. Although these methods provide near-correct image reconstruction, a significant computational cost prevents real-time applications. A simple alternative is a linear blending strategy which decomposes a single 2D image using depth information. This method provides real-time performance, but it generates inaccurate results at occlusion boundaries and on glossy surfaces. This paper proposes a perception-based hybrid decomposition technique which combines the advantages of the above strategies and achieves both real-time performance and high-fidelity results. The fundamental idea is to apply expensive optimizations only in regions where it is perceptually superior, e.g., depth discontinuities at the fovea, and fall back to less costly linear blending otherwise. We present a complete, perception-informed analysis and model that locally determine which of the two strategies should be applied. The prediction is later utilized by our new synthesis method which performs the image decomposition. The results are analyzed and validated in user experiments on a custom multi-plane display.

New augmented-reality near-eye displays provide capabilities for enriching real-world visual experiences with digital content. Most current research focuses on improving both hardware and software to provide digital content that seamlessly blends with the real world. This is believed to not only contribute to the visual experience but also increase human task performance. In this work, we take a step further and ask the question of whether the capabilities of current and future display designs combined with efficient perceptioninspired content optimizations can be used to improve human task performance beyond the human capabilities in the natural world. Based on an in-depth analysis of previous literature, we hypothesize here that such enhancements can be achieved when the human visual system is provided with content that optimizes the oculomotor responses. To further investigate possible gains, we present a series of perceptual experiments that built upon this idea. More specifically, we focus on speeding up accommodation response, which significantly contributes to the eye-adaptation when a new stimulus is shown. Through our experiments, we demonstrate that such speedups can be achieved, and more importantly, they can lead to significant improvements in human task performance. While not all of our results give definite answers, we believe that they reveal plentiful opportunities for further enhancing the human experience and task performance when using new augmented-reality displays.