Quantitative Evaluation of Human Visual Perception for Multiple Screens and Multiple Codecs

AbstractThis paper is a review and analysis of the various implementation architectures of diffractive waveguide combiners for augmented reality (AR), mixed reality (MR) headsets, and smart glasses. Extended reality (XR) is another acronym frequently used to refer to all variants across the MR spectrum. Such devices have the potential to revolutionize how we work, communicate, travel, learn, teach, shop, and are entertained. Already, market analysts show very optimistic expectations on return on investment in MR, for both enterprise and consumer applications. Hardware architectures and technologies for AR and MR have made tremendous progress over the past five years, fueled by recent investment hype in start-ups and accelerated mergers and acquisitions by larger corporations. In order to meet such high market expectations, several challenges must be addressed: first, cementing primary use cases for each specific market segment and, second, achieving greater MR performance out of increasingly size-, weight-, cost- and power-constrained hardware. One such crucial component is the optical combiner. Combiners are often considered as critical optical elements in MR headsets, as they are the direct window to both the digital content and the real world for the user’s eyes.Two main pillars defining the MR experience are comfort and immersion. Comfort comes in various forms: –wearable comfort—reducing weight and size, pushing back the center of gravity, addressing thermal issues, and so on–visual comfort—providing accurate and natural 3-dimensional cues over a large field of view and a high angular resolution–vestibular comfort—providing stable and realistic virtual overlays that spatially agree with the user’s motion–social comfort—allowing for true eye contact, in a socially acceptable form factor.Immersion can be defined as the multisensory perceptual experience (including audio, display, gestures, haptics) that conveys to the user a sense of realism and envelopment. In order to effectively address both comfort and immersion challenges through improved hardware architectures and software developments, a deep understanding of the specific features and limitations of the human visual perception system is required. We emphasize the need for a human-centric optical design process, which would allow for the most comfortable headset design (wearable, visual, vestibular, and social comfort) without compromising the user’s sense of immersion (display, sensing, and interaction). Matching the specifics of the display architecture to the human visual perception system is key to bound the constraints of the hardware allowing for headset development and mass production at reasonable costs, while providing a delightful experience to the end user.

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Integrating Human Visual Perception in Scene Understanding by Saliency Visual SLAM

2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) ◽

10.23919/sice48898.2020.9240349 ◽

2020 ◽

Author(s):

Naoki Kataoka ◽

Prarinya Siritanawan ◽

Kazunori Kotani

Keyword(s):

Visual Perception ◽

Scene Understanding ◽

Visual Slam ◽

Human Visual Perception

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Social Attribution and Explanation

10.1093/oxfordhb/9780199399550.013.33 ◽

2017 ◽

Author(s):

Denis Hilton

Keyword(s):

Visual Perception ◽

Final Section ◽

Higher Order ◽

Situation Models ◽

Causal Connection ◽

Human Visual Perception ◽

Social Attribution ◽

Text Understanding ◽

Causal Selection ◽

Partial Correction

Attribution processes appear to be an integral part of human visual perception, as low-level inferences of causality and intentionality appear to be automatic and are supported by specific brain systems. However, higher-order attribution processes use information held in memory or made present at the time of judgment. While attribution processes about social objects are sometimes biased, there is scope for partial correction. This chapter reviews work on the generation, communication, and interpretation of complex explanations, with reference to explanation-based models of text understanding that result in situation models of narratives. It distinguishes between causal connection and causal selection, and suggests that a factor will be discounted if it is not perceived to be connected to the event and backgrounded if it is perceived to be causally connected to that event, but is not selected as relevant to an explanation. The final section focuses on how interpersonal explanation processes constrain causal selection.

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