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Prediction-based realistic 3D model compression

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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.

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Acknowledgement

This paper is supported by 973 Program (2011CB302703), the National Natural Science Foundation of China (No. 60825203, 61033004, 60973056, 61170103, 61003182,U0935004), Beijing Natural Science Foundation (4102009, 4112007).

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Authors and Affiliations

  1. Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China

    Yunhui Shi, Bo Wen, Wenpeng Ding, Na Qi & Baocai Yin

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  1. Yunhui Shi
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  2. Bo Wen
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  3. Wenpeng Ding
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  4. Na Qi
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  5. Baocai Yin
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Correspondence to Bo Wen.

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Shi, Y., Wen, B., Ding, W. et al. Prediction-based realistic 3D model compression. Multimed Tools Appl 70, 2125–2137 (2014). https://doi.org/10.1007/s11042-012-1231-9

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  • DOI: https://doi.org/10.1007/s11042-012-1231-9

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