Prediction-based realistic 3D model compression
Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images wh...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shi, Yunhui [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 70(2012), 3 vom: 11. Sept., Seite 2125-2137 |
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| Übergeordnetes Werk: |
volume:70 ; year:2012 ; number:3 ; day:11 ; month:09 ; pages:2125-2137 |
| Links: |
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| DOI / URN: |
10.1007/s11042-012-1231-9 |
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OLC2035010551 |
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| 520 | |a Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. | ||
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| 700 | 1 | |a Ding, Wenpeng |4 aut | |
| 700 | 1 | |a Qi, Na |4 aut | |
| 700 | 1 | |a Yin, Baocai |4 aut | |
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10.1007/s11042-012-1231-9 doi (DE-627)OLC2035010551 (DE-He213)s11042-012-1231-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shi, Yunhui verfasserin aut Prediction-based realistic 3D model compression 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. Geometry image Normal-map image Geometric correlation Prediction Wen, Bo aut Ding, Wenpeng aut Qi, Na aut Yin, Baocai aut Enthalten in Multimedia tools and applications Springer US, 1995 70(2012), 3 vom: 11. Sept., Seite 2125-2137 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:70 year:2012 number:3 day:11 month:09 pages:2125-2137 https://doi.org/10.1007/s11042-012-1231-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 70 2012 3 11 09 2125-2137 |
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10.1007/s11042-012-1231-9 doi (DE-627)OLC2035010551 (DE-He213)s11042-012-1231-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shi, Yunhui verfasserin aut Prediction-based realistic 3D model compression 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. Geometry image Normal-map image Geometric correlation Prediction Wen, Bo aut Ding, Wenpeng aut Qi, Na aut Yin, Baocai aut Enthalten in Multimedia tools and applications Springer US, 1995 70(2012), 3 vom: 11. Sept., Seite 2125-2137 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:70 year:2012 number:3 day:11 month:09 pages:2125-2137 https://doi.org/10.1007/s11042-012-1231-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 70 2012 3 11 09 2125-2137 |
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10.1007/s11042-012-1231-9 doi (DE-627)OLC2035010551 (DE-He213)s11042-012-1231-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shi, Yunhui verfasserin aut Prediction-based realistic 3D model compression 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. Geometry image Normal-map image Geometric correlation Prediction Wen, Bo aut Ding, Wenpeng aut Qi, Na aut Yin, Baocai aut Enthalten in Multimedia tools and applications Springer US, 1995 70(2012), 3 vom: 11. Sept., Seite 2125-2137 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:70 year:2012 number:3 day:11 month:09 pages:2125-2137 https://doi.org/10.1007/s11042-012-1231-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 70 2012 3 11 09 2125-2137 |
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10.1007/s11042-012-1231-9 doi (DE-627)OLC2035010551 (DE-He213)s11042-012-1231-9-p DE-627 ger DE-627 rakwb eng 070 004 VZ Shi, Yunhui verfasserin aut Prediction-based realistic 3D model compression 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2012 Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. Geometry image Normal-map image Geometric correlation Prediction Wen, Bo aut Ding, Wenpeng aut Qi, Na aut Yin, Baocai aut Enthalten in Multimedia tools and applications Springer US, 1995 70(2012), 3 vom: 11. Sept., Seite 2125-2137 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:70 year:2012 number:3 day:11 month:09 pages:2125-2137 https://doi.org/10.1007/s11042-012-1231-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 70 2012 3 11 09 2125-2137 |
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Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. © Springer Science+Business Media, LLC 2012 |
| abstractGer |
Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. © Springer Science+Business Media, LLC 2012 |
| abstract_unstemmed |
Abstract The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced. © Springer Science+Business Media, LLC 2012 |
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Prediction-based realistic 3D model compression |
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https://doi.org/10.1007/s11042-012-1231-9 |
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Wen, Bo Ding, Wenpeng Qi, Na Yin, Baocai |
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10.1007/s11042-012-1231-9 |
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2024-07-03T23:25:24.210Z |
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