Object-based depth image–based rendering for a three-dimensional video system by color-correction optimization

Shao, Feng; Jiang, Gangyi; Yu, Mei; Zhang, Yun

doi:10.1117/1.3569627

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Nowadays, 3D systems rather employ depth information to improve the quality experience by, for example, increasing the number of views that could be displayed at the receiver side [4,5]. This is possible because of depth image based rendering (DIBR) techniques [6,7] that project one reference image onto virtual views using depth as geometrical information. Figure 1 shows the overall structure of a DIBR multiview coder that is also considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

Rate-distortion analysis of multiview coding in a DIBR framework

et al. 2013

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Depth image based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. Encoding rate control has thus to consider both texture and depth data. Due to different structures of depth and texture images and their different roles on the rendered views, distributing the available bit budget between them however requires a careful analysis. Information loss due to texture coding affects the value of pixels in synthesized views while errors in depth information lead to shift in objects or unexpected patterns at their boundaries. In this paper, we address the problem of efficient bit allocation between textures and depth data of multiview video sequences. We adopt a rate-distortion framework based on a simplified model of depth and texture images. Our model preserves the main features of depth and texture images. Unlike most recent solutions, our method permits to avoid rendering at encoding time for distortion estimation so that the encoding complexity is not augmented. In addition to this, our model is independent of the underlying in-

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Section: Introductionmentioning

confidence: 99%

Rate-distortion analysis of multiview coding in a DIBR framework

et al. 2013

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“…First, the dense projection (pixel-based operation) and the inpainting algorithms (block-matching algorithm) are both very complex for a low power decoder [16]. Secondly, if the hole filling algorithm does not use any information taken from the neighboring frames, it reconstructs the images without really guaranteeing temporal or inter-view consistency.…”

Section: B Requirements Posed By Interactivitymentioning

confidence: 99%

Interactive Multiview Video System With Low Complexity 2D Look Around at Decoder

Maugey

Frossard

2013

IEEE Trans. Multimedia

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Abstract-Multiview video with interactive 2D look around at the receiver is a challenging application with several issues in terms of effective use of storage and bandwidth resources, reactivity of the system, quality of the viewing experience and system complexity. The impression of 3D immersion is highly dependent on the smoothness of the navigation and thus on the number of 2D viewpoints. The classical decoding system for generating virtual views first projects a reference or encoded frame to a given viewpoint and then fills in the holes due to potential occlusions. This last step still constitutes a complex operation with specific software or hardware at the receiver and requires a certain quantity of information from the neighboring frames for ensuring consistency between the virtual images. In this work we propose a new approach that shifts most of the burden due to interactivity from the decoder to the encoder, by anticipating the navigation of the decoder and sending auxiliary information that guarantees temporal and interview consistency. This leads to an additional cost in terms of transmission rate and storage, which we minimize by using optimization techniques based on the user behavior modeling. We show by experiments that the proposed system represents a valid solution for interactive multiview systems with classical decoders.Index Terms-2D look around viewing, interactivity, multiview video coding, view synthesis.

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“…Obviously this brings interesting challenges for 3D transmission systems. For example, a depth image can be used to project one reference image onto another one [2], [3] with interesting benefits in the compression of multiview images. Despite the huge potential of this tool, one of the important questions linked with depth images relies in the effect of compression on the view prediction performance [4].…”

Section: Introductionmentioning

confidence: 99%

Graph-based vs depth-based data representation for multiview images

Maugey

Ortega

Frossard

2013

2013 Asilomar Conference on Signals, Systems and Computers

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Abstract-In this paper, we propose a representation and coding method for multiview images. As an alternative to depthbased schemes, we propose a representation that captures the geometry and the dependencies between pixels in different views in the form of connections in a graph. In our approach it is possible to perform compression of the geometry information and to preserve a direct control of the effect of geometry approximation on view reconstruction. This is not possible with classical depth-based representations. As a results, our method leads to more accurate view prediction, when compared to conventional lossy coding of depth maps operating at the same bit rate. We finally show in experiments that our representation adapts the amount of transmitted geometry to the complexity of the predictions that are performed at the decoder.

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Object-based depth image–based rendering for a three-dimensional video system by color-correction optimization

Cited by 5 publications

References 25 publications

Rate-distortion analysis of multiview coding in a DIBR framework

Rate-distortion analysis of multiview coding in a DIBR framework

Interactive Multiview Video System With Low Complexity 2D Look Around at Decoder

Graph-based vs depth-based data representation for multiview images

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