Stereoscopic and autostereoscopic display systems

Sexton, Ian; Surman, Phil

doi:10.1109/79.768575

Cited by 115 publications

(52 citation statements)

References 30 publications

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“…Among them, multiview content representation and coding is a vital topic as part of which, compression efficiency, flexibility and interactivity are considered. There are three main types of 3D video, namely stereoscopic video [1]- [3], multiview video [4] and free viewpoint video [5], [6]. Multiview video coding (MVC) is a popular format, which allows the user to interactively switch viewing angle without the necessity to wear special glasses.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Tillo

Xiao

et al. 2018

IEEE Trans. Multimedia

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Abstract-Multiview video streaming continues to gain popularity due to the great viewing experience it offers, as well as its availability which has been enabled by increased network throughput and other recent technical developments. User demand for interactive multiview video streaming that provides seamless view switching upon request is also increasing. However, it is a highly challenging task to stream stable and high quality videos that allow real time scene navigation within the bandwidth constraint. In this paper a convolutional neural network (ConvNet) assisted seamless multiview video streaming system is proposed to tackle the challenge. The proposed method solves the problem from two perspectives: first, a ConvNet assisted multiview representation method is proposed, which provides flexible interactivity without compromising on multiview video compression efficiency. Second, a bit allocation mechanism guided by a navigation model is developed to provide seamless navigation and adapt to network bandwidth fluctuations at the same time. These two blocks work closely to provide an optimized viewing experience to users. They can be integrated into any existing multiview video streaming framework to enhance overall performance. Experimental results demonstrate the effectiveness of the proposed method for seamless multiview streaming.Index Terms-Multiview video streaming, Multiview navigation, Multiview video representation, Convolutional neural network.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Tillo

Xiao

et al. 2018

IEEE Trans. Multimedia

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“…No wonder that a lot of effort has been put recently to add the 3 rd dimension into imaging technology. Nowadays, the biggest commercial implementation of 3D imaging and display is mainly real− ized by stereoscopic, pseudostereoscopic and integral imag− ing techniques which are based on 2D images and their transformation by our brain [1,2] In order to provide a "true− −3D" display, it is necessary to deliver a high quality optical replica of an object or scene that can be viewed by an observer freely from different directions. This can be achi− eved with holography that stands out from the other 3D imaging techniques [3] due to the ability to capture and cor− rectly reconstruct 3D object wavefront [4,5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal multiplexing method for visual field of view extension in holographic displays with naked eye observation

Finke

Kujawińska

Kozacki

et al. 2016

Opto-Electronics Review

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“…On the other hand current 3D TV stereoscopic and autostereoscopic imaging technique rely on 2D images only [1][2][3]. This simplification has enabled commercialization of 3D display techniques in the form of 3D cinema and television, however it does not allow the viewer to satisfy all cues of human visual 3D perception as is true in the case of holography [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multiwavefront digital holographic television

Kujawińska¹,

Kozacki²,

Falldorf³

et al. 2014

Opt. Express

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This paper presents the full technology chain supporting wide angle digital holographic television from holographic capture of real world objects/scenes to holographic display with an extended viewing angle. The data are captured with multiple CCD cameras located around an object. The display system is based on multiple tilted spatial light modulators (SLMs) arranged in a circular configuration. The capture-display system is linked by a holographic data processing module, which allows for significant decoupling of the capture and display systems. The presented experimental results, based on the reconstruction of real world, variable in time scenes, illustrates imaging dynamics, viewing angle and quality. 857-867 (1998).

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Stereoscopic and autostereoscopic display systems

Cited by 115 publications

References 30 publications

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

Spatiotemporal multiplexing method for visual field of view extension in holographic displays with naked eye observation

Multiwavefront digital holographic television

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