On constructing z dimensional Image By DIBR Synthesized Images it is based on DIBR rendering and 3d rendering

1. On Constructing z-Dimensional
DIBR-Synthesized Images

2. What is DIBR?
 DIBR stands for Depth Image Based Rendering
 Image-Based Rendering (IBR) is an emerging technology which enables the
synthesis of novel realistic images of a scene from virtual viewpoints, using a
collection of available images. The applications of IBR can be found in various
situations such as virtual reality, telepresence, thanks to the complexity and
performance advantage over model-based techniques, which bases on complex 3-
D geometric models, material properties and lightening conditions of the scene
 DIBR is IBR technique which maps each color pixel in a reference view to a 2D grid
location in the virtual view, using disparity information provided by the
corresponding depth pixel.

3. What is Rendering and Z Dimension?
 Rendering is the process of generating an image from a 2D or 3D model (or
models in what collectively could be called a scene file) by means of computer
programs.
 Three-dimensional space (also: 3-space or, rarely, tri-dimensional space) is a
geometric setting in which three values (called parameters) are required to
determine the position of an element (i.e., point). This is the informal meaning of
the term dimension.

4. CONSTRUCTION OF Z DIMENSION
 To construct this new image type, we first perform a new DIBR pixel-mapping for z-dimensional
camera movement.
 We then identify expansion holes—a new kind of missing pixels unique in z-dimensional DIBR-
mapped images—using a depth layering procedure.
 To fill expansion holes we formulate a patch-based maximum a posteriori problem, where the
patches are appropriately spaced using diamond tiling.
 Leveraging on recent advances in graph signal processing, we define a graph-signal smoothness
prior to regularize the inverse problem.
 Finally, we design a fast iterative reweighted least square algorithm to solve the posed problem
efficiently. Experimental results show that our z-dimensional synthesized images outperform
images rendered by a native modification

10. Virtual Camera
 a virtual camera system aims at controlling a camera or a set of cameras to display
a view of a 3D virtual world. Camera systems where their purpose is to show the
action at the best possible angle; more generally, they are used in 3D virtual worlds
when a third person view is required.

12. Constructing z-Dimensional DIBR-Synthesized
Images
Divided into three Sections
1.DIBR (Depth Image Based Rendering)
2.Image Super Resolution
3.Graph Based Image Processing

13. Depth Image Based Rendering
 Color-plus-depth format , consisting of one or more color and depth image pairs
from different viewpoints, is a widely used 3D scene representation. Using this
format, low-complexity DIBR view synthesis procedure such as 3D warping [ can be
used to create credible virtual view images, with the aid of in painting algorithms to
complete disocclusion holes
 In this work, we assume that enough pixels from one or more reference view(s)
have been transmitted to the decoder for virtual view synthesis, and we focus only
on the construction of z-dimensional DIBR-synthesized images given received
reference view pixels.

14. Image Super Resolution
 Increase in object size due to large z-dimensional virtual camera movement is
analogous to increasing the resolution (super-resolution (SR)) of the whole image.
However, during z-dimensional camera motion an object closer to the camera
increases in size faster than objects farther away, while in SR, resolution is increased
uniformly for all spatial regions in the image.
 For the above reason, we cannot directly apply conventional image SR techniques
[30] in rectangular pixel grid to interpolate the synthesized view. Further, recent
non-local SR techniques such as leveraging on self-similarity of natural images that
require an exhaustive search of similar patches throughout an image tend to be
computationally expensive. In contrast, our interpolation scheme performs only
iterative local filtering, and thus is significantly more computation-efficient.

15. Graph Based Image Processing
 GSP is the study of signals that live on structured data kernels described by graphs
, leveraging on spectral graph theory for frequency analysis of graph-signals.
 Graph-signal priors have been derived for inverse problems such as denoising ,
interpolation , bit-depth enhancement and de-quantization.
 In this work, we assume the latter case and construct a suitable graph G from
available DIBR-synthesized pixels for joint denoising/interpolation of pixels in a
target patch.

16. SYSTEM OVERVIEW
 Interactive Free Viewpoint Streaming System
 DIBR
 Rounding Noise in mapped pixels
 Identification of expansion holes

17. Interactive Free Viewpoint Streaming System

18. Depth-Image-Based Rendering

19. Rounding Noise in DIBR-Mapped Pixels

20. Identification of Expansion Holes

21. CONCLUSION
 Unlike typical free viewpoint system that considers only synthesis of virtual views
shifted horizontally along the x dimension via DIBR, in this paper we consider in
addition construction of z-dimensional DIBR-synthesized images. In such far-to-
near viewpoint synthesis, there exists a new type of missing pixels called expansion
holes – where objects close to the camera will increase in size and simple pixel-to-
pixel mapping in DIBR from reference to virtual view will result in missing pixel
areas – that demand a new interpolation scheme.

22. THANKS
 P. Merkle, A. Smolic, K. Mueller, and T. Wiegand, “Multi-view video plus depth
representation and coding,
 A. Chuchvara, M. Georgiev, and A. Gotchev, “CPU-efficient free view synthesis based
on depth layering,” in Proc. 3DTV-Conf: True Vis. - Capture, Transmiss. Display 3D
Video, Jul. 2014,
 M. Tanimoto, M. P. Tehrani, T. Fujii, and T. Yendo, “Free-viewpoint TV,” IEEE Signal
Process. Mag., vol. 28, no. 1, pp. 67–76, Jan. 2011.
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