An adaptive quantization scheme for efficient texture coordinate compression in MPEG 3DMC

  • Authors:

  • Sunyoung Lee;Byeongwook Min;Daiyong Kim;Eun-Young Chang;Namho Hur;Soo In Lee;Euee S. Jang

  • Affiliations:

  • Digital Media Lab., College of Information and Communications, Hanyang University, Seoul, Korea;Digital Media Lab., College of Information and Communications, Hanyang University, Seoul, Korea;Digital Media Lab., College of Information and Communications, Hanyang University, Seoul, Korea;Digital Broadcasting Research Division, Broadcasting System Research Group, Electronics and Telecommunications Research Institute, Daejeon, Korea;Digital Broadcasting Research Division, Broadcasting System Research Group, Electronics and Telecommunications Research Institute, Daejeon, Korea;Digital Broadcasting Research Division, Broadcasting System Research Group, Electronics and Telecommunications Research Institute, Daejeon, Korea;Digital Media Lab., College of Information and Communications, Hanyang University, Seoul, Korea

  • Venue:
  • PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
  • Year:
  • 2005

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Abstract

Graphic models represented by 3D polygonal mesh (with geometry, color, normal vector, and texture coordinate information) are now heavily used in interactive multimedia applications. As an international standard, MPEG-4 3D mesh coding (3DMC) is a compression tool for 3D mesh models. Texture mapping on a 3D mesh model is now getting popular, since it can produce high quality reconstruction even with small number of polygons used. Texture coordinates (TCs) play an important role in texture mapping. In compressing TCs using 3DMC, we found that the lossless compression is not guaranteed, which jeopardizes the texture mapping due to the precision error between the original and reconstructed TC values. In this paper, we proposed an adaptive quantization scheme for efficient TC compression in 3DMC. By adaptively choosing the step size for quantization, the proposed method can guarantee the lossless compression.