|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM246176369 |
| 003 |
DE-627 |
| 005 |
20250218044830.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231224s2015 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TIP.2015.2400817
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0820.xml
|
| 035 |
|
|
|a (DE-627)NLM246176369
|
| 035 |
|
|
|a (NLM)25675455
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Maugey, Thomas
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Graph-based representation for multiview image geometry
|
| 264 |
|
1 |
|c 2015
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 19.05.2015
|
| 500 |
|
|
|a Date Revised 18.03.2015
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a In this paper, we propose a new geometry representation method for multiview image sets. Our approach relies on graphs to describe the multiview geometry information in a compact and controllable way. The links of the graph connect pixels in different images and describe the proximity between pixels in 3D space. These connections are dependent on the geometry of the scene and provide the right amount of information that is necessary for coding and reconstructing multiple views. Our multiview image representation is very compact and adapts the transmitted geometry information as a function of the complexity of the prediction performed at the decoder side. To achieve this, our graph-based representation (GBR) carefully selects the amount of geometry information needed before coding. This is in contrast with depth coding, which directly compresses with losses the original geometry signal, thus making it difficult to quantify the impact of coding errors on geometry-based interpolation. We present the principles of this GBR and we build an efficient coding algorithm to represent it. We compare our GBR approach to classical depth compression methods and compare their respective view synthesis qualities as a function of the compactness of the geometry description. We show that GBR can achieve significant gains in geometry coding rate over depth-based schemes operating at similar quality. Experimental results demonstrate the potential of this new representation
|
| 650 |
|
4 |
|a Journal Article
|
| 650 |
|
4 |
|a Research Support, Non-U.S. Gov't
|
| 700 |
1 |
|
|a Ortega, Antonio
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Frossard, Pascal
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 24(2015), 5 vom: 11. Mai, Seite 1573-86
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
|
| 773 |
1 |
8 |
|g volume:24
|g year:2015
|g number:5
|g day:11
|g month:05
|g pages:1573-86
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TIP.2015.2400817
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 24
|j 2015
|e 5
|b 11
|c 05
|h 1573-86
|