Short Communication to SMI 2011: Bayesian AD coder: Mesh-aware valence coding for multiresolution meshes

  • Authors:
  • Junho Kim;Changwoo Nam;Sungyul Choe

  • Affiliations:
  • School of Computer Science, Kookmin University, 861-1 Jeongneung-Dong, Seongbuk-Gu, Seoul 136-702, Republic of Korea;School of Computer Science, Kookmin University, 861-1 Jeongneung-Dong, Seongbuk-Gu, Seoul 136-702, Republic of Korea;1040-2 Yeongtong-Dong, Yeongtong-Gu, Suwon-Si 443-470, Republic of Korea

  • Venue:
  • Computers and Graphics
  • Year:
  • 2011

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Abstract

The Alliez Desbrun (AD) coder has accomplished the best compression ratios for multiresolution 2-manifold meshes in the last decade. This paper presents a Bayesian AD coder which has better compression ratios in connectivity coding than the original coder, based on a mesh-aware valence coding scheme for multiresolution meshes. In contrast to the original AD coder, which directly encodes a valence for each decimated vertex, our coder indirectly encodes the valence according to its rank in a sorted list with respect to the mesh-aware scores of the possible valences. Experimental results show that the Bayesian AD coder shows an improvement of 8.5-36.2% in connectivity coding compared to the original AD coder despite of the fact that a simple coarse-to-fine step of the mesh-aware valence coding is plugged into the original algorithm.