Depth map compression for real-time view-based rendering

Chai, Bing-Bing; Sethuraman, Sriram; Sawhney, Harpreet; Hatrack, P.

doi:10.1016/j.patrec.2004.01.002

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…Compression of mappings has also been studied. Chai et al [8] proposed the depth map compression algorithm by encoding mappings as simplified triangular meshes. Lewis and English [33] described a technique for compressing surface potential mapping data using transform techniques.…”

Section: Related Workmentioning

confidence: 99%

Texture Map and Video Compression Using Beltrami Representation

Lui¹,

Lam²,

Wong³

et al. 2013

SIAM J. Imaging Sci.

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Surface parameterizations and registrations are important in computer graphics and imaging, where 1-1 correspondences between meshes are computed. In practice, surface maps are usually represented and stored as three-dimensional coordinates each vertex is mapped to, which often requires lots of memory. This causes inconvenience in data transmission and data storage. To tackle this problem, we propose an effective algorithm for compressing surface homeomorphisms using Fourier approximation of the Beltrami representation. The Beltrami representation is a complex-valued function defined on triangular faces of the surface mesh with supreme norm strictly less than 1. Under suitable normalization, there is a 1-1 correspondence between the set of surface homeomorphisms and the set of Beltrami representations. Hence, every bijective surface map is associated with a unique Beltrami representation. Conversely, given a Beltrami representation, the corresponding bijective surface map can be exactly reconstructed using the linear Beltrami solver introduced in this paper. Using the Beltrami representation, the surface homeomorphism can be easily compressed by Fourier approximation, without distorting the bijectivity of the map. The storage requirement can be effectively reduced, which is useful for many practical problems in computer graphics and imaging. In this paper, we propose applying the algorithm to texture map compression and video compression. With our proposed algorithm, the storage requirement for the texture properties of a textured surface can be significantly reduced. Our algorithm can further be applied to compressing motion vector fields for video compression, which effectively improves the compression ratio.

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Section: Related Workmentioning

confidence: 99%

Texture Map and Video Compression Using Beltrami Representation

Lui¹,

Lam²,

Wong³

et al. 2013

SIAM J. Imaging Sci.

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“…Other methods [Chai et al 2004;Kim and Ho 2007] focus on the reconstruction of a 3D representation optimised for quick graphic rendering. Triangle meshes are chosen for this purpose, since they allow to represent 3D surfaces much more compactly than depth maps, and since graphic hardware is strongly optimised for triangle rendering.…”

Section: Related Workmentioning

confidence: 99%

Real-time compression of depth streams through meshification and valence-based encoding

Bannò

Gasparello

Tecchia

et al. 2012

Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry

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Free viewpoint telepresence is currently an active and very interesting field of research. Its focus is reproducing in real-time a remote subject (or environment) in a way it can be rendered from an arbitrary point-of-view. This paper presents a novel approach to real-time reconstruction and transmission of a high-quality 3D model of the subject/environment based on the meshification of the depth map produced by a range camera system (i.e. time-of-flight, Kinect). The generated mesh is compressed through a variant of the state-of-the-art single-rate mesh compression algorithm, ready to be transmitted over the network towards the rendering location in a fast and efficient way.

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“…Nor have comparisons been done with a current state-of-theart video encoder, neither have the different tree-based approaches been compared to each other. Here, we compare quad trees [5] [11], as well as own enhancements aimed at improving coding efficiency. We also compare with the 3DVC module [14] from the latest video standard (HEVC [17]).…”

Section: Introductionmentioning

confidence: 99%

What Is the Best Depth-Map Compression for Depth Image Based Rendering?

Ogniewski

Forssén

2017

Computer Analysis of Images and Patterns

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Many of the latest smart phones and tablets come with integrated depth sensors, that make depth-maps freely available, thus enabling new forms of applications like rendering from different view points. However, efficient compression exploiting the characteristics of depth-maps as well as the requirements of these new applications is still an open issue. In this paper, we evaluate different depth-map compression algorithms, with a focus on tree-based methods and view projection as application. The contributions of this paper are the following: 1. extensions of existing geometric compression trees, 2. a comparison of a number of different trees, 3. a comparison of them to a state-of-the-art video coder, 4. an evaluation using ground-truth data that considers both depth-maps and predicted frames with arbitrary camera translation and rotation. Despite our best efforts, and contrary to earlier results, current video depth-map compression outperforms tree-based methods in most cases. The reason for this is likely that previous evaluations focused on low-quality, low-resolution depth maps, while high-resolution depth (as needed in the DIBR setting) has been ignored up until now. We also demonstrate that PSNR on depth-maps is not always a good measure of their utility.

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Depth map compression for real-time view-based rendering

Cited by 29 publications

References 13 publications

Texture Map and Video Compression Using Beltrami Representation

Texture Map and Video Compression Using Beltrami Representation

Real-time compression of depth streams through meshification and valence-based encoding

What Is the Best Depth-Map Compression for Depth Image Based Rendering?

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