A Framework for Multi-view Video Coding Using Layered Depth Images

Yoon, Seung-Uk; Lee, Eunkyung; Kim, Sung-Yeol; Ho, Yo‐Sung

doi:10.1007/11581772_38

Cited by 22 publications

(12 citation statements)

References 3 publications

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“…and Processing of the Multi-view Video Using LDI Figure 9 shows the overall system diagram for representation and processing of multi-view vide using the concept of LDI [2]. As shown in Fig.…”

Section: Framework For Representationmentioning

confidence: 99%

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A Framework for Representation and Processing of Multi-view Video Using the Concept of Layered Depth Image

Yoon

Lee

Kim

et al. 2007

J VLSI Sign Process Syst Sign Image Video Technol

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The multi-view video is a collection of multiple videos, capturing the same scene at different viewpoints. Since it contains more affluent information than a single video, it can be applied to various applications, such as 3DTV, free viewpoint TV, surveillance, sports matches, and so on. However, the data size of the multi-view video linearly increases as the number of cameras, therefore it is necessary to develop an effective framework to represent, process, and transmit those huge amounts of data. In recent, multi-view video coding is getting lots of attention as efficient video coding technologies are being developed. Although most of multi-view video coding algorithms are based on the state-of-the-art H.264/AVC video coding technology, they do not utilize rich 3-D information. In this paper, we propose a new framework using the concept of layered depth image (LDI), one of the efficient image-based rendering techniques, to efficiently represent and process multi-view video data. We describe how to represent natural multi-view video based on the LDI approach and the overall framework to process those converted data.

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Section: Framework For Representationmentioning

confidence: 99%

“…We describe how to generate layered depth images (LDIs) from the natural multi-view video and the overall framework to process those converted data [2]. While most of the proposed MVC techniques are some extensions of predictive video coding algorithms, our framework takes a completely different approach based on the concept of LDI.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Representation and Processing of Multi-view Video Using the Concept of Layered Depth Image

Yoon

Lee

Kim

et al. 2007

J VLSI Sign Process Syst Sign Image Video Technol

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“…Previous approaches have focused on layer-based coding like Fig. 1 [7], and NFNL [4], treat each layer of LDI as a 2D image. And some few of them, such as CMP [10], generate new 2D images and focuses on how to represent the data of LDI compactly and efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…However, because of the special data structure of LDI, they cannot be applied directly or are not very efficient. So, several researches have been carried on the compression of LDI: compression for real world scenes from natural multiview video [4][5] [6] [7][8] [16], lossy compression for synthetic static scenes [9] [11], lossy compression for static objects [10]. Previous approaches have focused on layer-based coding like Fig.…”

Section: Introductionmentioning

confidence: 99%

Depth Compression of 3D Object Represented by Layered Depth Image

Park

Kim

2010

2010 Data Compression Conference

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A Layered Depth Image (LDI) is one of the popular representation and rendering methods for 3D objects with complex geometries. In this paper, we propose the new compression algorithm for depth information of a 3D object represented by LDI. For the purpose, we introduce the concept of partial surfaces to seek highly correlated depth data irrespective of their layer and derive a depth compression algorithm by using them. Partial surfaces are approximated by a Bézier patch and residual information is encoded by a shape-adaptive transform. Experimental results show that our proposed compression method achieves a better compression performance than any other previous methods. INTRODUCTIONImage-based modeling and rendering techniques have received much attention as a powerful alternative to the traditional geometry-based techniques for generating novel views of real/synthetic scenes or objects. Lots of useful geometry-based modeling and rendering techniques have been developed. However, they require elaborate modeling and long processing time to reconstruct and render complex objects. And they provide unsatisfactory quality in producing truly photorealistic objects. As an attractive alternative to overcome these problems, many of image-based modeling and rendering (IBR) techniques have been proposed in the past decade. They use 2D images as primitives to generate an arbitrary view of the 3D scene [19]. Among the variety of IBR techniques, a layered depth image (LDI) [1] is one of the efficient rendering methods for 3D objects. LDI represents the object with an array of pixels viewed from a single reference camera. As a data structure corresponding to a single projection of scenes, it stores information for each intersection point of each projection ray, which start at a fiducial point, with objects. And a layered depth pixel stores a set of depth pixels along one line of sight sorted in front to back order. The front pixel in the layered depth pixels samples the first surface seen along that line of sight; the next pixel samples the next surface seen along that line of sight, etc. Although LDI is one of the efficient rendering methods, one big obstacle is that it has a huge amount of data. In addition, because of multiple layer characteristic of LDI, the number of layers (NOL) must be additionally encoded.There are many of lossy 2D image compression algorithms and schemes. However, because of the special data structure of LDI, they cannot be applied directly or are not very efficient. So, several researches have been carried on the compression of LDI: compression for real world scenes from natural multiview video [4][5][6][7][8][16], lossy compression for synthetic static scenes [9][11], lossy compression for static objects [10]. Previous approaches have focused on layer-based coding like Fig. 1(a): Most of them, such as DA [2][3][5][6][7][9][11][16], MD [8], NFFL [2][3][5][6][7], and NFNL [4], treat each layer of LDI as a 2D image. And some few of them, such as CMP [10], generate new 2D images and focuses on how to re...

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“…The sheer amount of data attained from capturing cameras still poses a significant challenge in the image processing and transmission domains [4]. Moreover, implementation constraints dictate that practical systems can only be achieved if the amount of instrumentation employed for scene sampling is pruned, since this, would also constitute a linear decrease in the raw video data that would require processing [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient multiview depth representation based on image segmentation

Raffaele

Camilleri

Debono

et al. 2012

2012 Picture Coding Symposium

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Abstract-The persistent improvements witnessed in multimedia production have considerably augmented users demand for immersive 3D systems. Expedient implementation of this technology however, entails the need for significant reduction in the amount of information required for representation. Depth image-based rendering algorithms have considerably reduced the number of images necessary for 3D scene reconstruction, nevertheless the compression of depth maps still poses several challenges due to the peculiar nature of the data. To this end, this paper proposes a novel depth representation methodology that exploits the intrinsic correlation present between colour intensity and depth images of a natural scene. A segmentation-based approach is implemented which decreases the amount of information necessary for transmission by a factor of 24 with respect to conventional JPEG algorithms whilst maintaining a quasi identical reconstruction quality of the 3D views.

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A Framework for Multi-view Video Coding Using Layered Depth Images

Cited by 22 publications

References 3 publications

A Framework for Representation and Processing of Multi-view Video Using the Concept of Layered Depth Image

A Framework for Representation and Processing of Multi-view Video Using the Concept of Layered Depth Image

Depth Compression of 3D Object Represented by Layered Depth Image

Efficient multiview depth representation based on image segmentation

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