Selective quality rendering by exploiting human inattentional blindness

Cater, Kirsten; Chalmers, Alan; Ledda, Patrick

doi:10.1145/585741.585744

Cited by 22 publications

(24 citation statements)

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“…Image regions receiving less visual attention may be rendered more approximately with larger errors [28]. Other properties of the human visual system, such as inattentional blindness and change blindness, have also been utilized for rendering acceleration [29], [30], [31], character animation [32], and tone mapping [33]. Perceptual strategies are also useful in participating media rendering [34], virtual crowd rendering [35], and rendering of motion blur effects [36].…”

Section: Perceptually Motivated Graphicsmentioning

confidence: 99%

Change Blindness Images

Wong

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.

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Section: Perceptually Motivated Graphicsmentioning

confidence: 99%

Change Blindness Images

Wong

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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“…Hayhoe demonstrated that the nature of the activity influences the set of attributes which are selected for storing in the "visual buffer". Cater et al [3] showed that the involvement of participants in a given task contributed towards inattentional blindness. An interesting theory proposed by Desimone et al [6] provides an explanation for the filtering process which is task-biased.…”

Section: Activity Dependancementioning

confidence: 99%

“…It also has to be populated with a large number of inhabitants carrying out a wide range of activities which would seem normal. 3 Figure 7 show snapshots of the city populated with inhabitants. Figure 8 shows the implementation of architecture II in the context of the inhabited city.…”

Section: Experimental Platformmentioning

confidence: 99%

“…Figure 8 shows the implementation of architecture II in the context of the inhabited city. A special client, the crowd 3 A detailed description of the working of the city and crowd simulator is not relevant to the paper, and has been omitted animator generates the states of crowd entities which are not controlled by the user. These entities include virtual inhabitants.…”

Section: Experimental Platformmentioning

confidence: 99%

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Visual attention based information culling for Distributed Virtual Environments

Beeharee

West²,

Hubbold

2003

Proceedings of the ACM Symposium on Virtual Reality Software and Technology

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The central goal in Distributed Virtual Environments (DVEs) is to provide an experience of the shared world that is perceptually plausible for the immersed user. This has to be achieved in the face of network issues such as bandwidth limitations and latencies which make total synchronism impossible. Techniques have been devised to help; some optimise network use by limiting what is transmitted, others try to disguise the effects of network 'glitches' at the user's client machine -for example smoothing policies. To date such techniques tend to be system-based rather than user-oriented. Yet there is an active body of psychological research, for example in visual attention, which has successfully been employed by the graphics community to yield better perceived results for a given resource. Immersive DVEs are even more critically dependent upon the users' perceived experience. We may expect therefore that such 'psychologically' oriented approaches have even more to offer here. In this paper a visual attention model which exploits the peculiarities of the human visual system is presented. It is based on previous work, and on a series of carefully designed experiments which are used to guide the implementation of the model and to design architectures for DVEs. The proposed architectures are then tested using different DVE experimental applications, which include a highly populated virtual city. The results demonstrate that the characteristics of the human visual system can be exploited to improve network usage and generate a more perceptually plausible environment in the face of incomplete synchronisation.

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“…In particular, we investigate to what level viewers fail to notice degradations in image quality, between non-task related areas and task related areas, when quality parameters such as image resolution, edge anti-aliasing and reflection and shadows are altered. Cater et al showed that conspicuous objects in a scene that would normally attract the viewer's attention are ignored if they are not relevant to the task at hand [Cater et al 2002]. This failure of the human to see unattended items in a scene, is known as inattentional blindness [Mack and Rock 1998].…”

Section: Introductionmentioning

confidence: 99%