Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Yayın
    Spectral coding of mesh geometry with a hierarchical set partitioning algorithm
    (Spie-Int Soc Optical Engineering, 2008) Konur, Umut; Bayazıt, Uluğ; Ateş, Hasan Fehmi; Gürgen, Sadık Fikret
    This work proposes a progressive mesh geometry coder, which expresses geometry information in terms of spectral coefficients obtained through a transformation and codes these coefficients using a hierarchical set partitioning algorithm that assigns right priorities to those coefficients at all bit planes. The spectral transformation used is the one proposed in [8] where the spectral coefficients are obtained by projecting the mesh geometry on an orthonormal basis determined by mesh topology. The set partitioning method used in coding, treats spectral coefficients belonging to the three spatial coordinates with the right priority at all bit planes and realizes a truly embedded system by achieving implicit bit allocation via joint coding the zeroes of coefficients at the bit planes. The experiments performed on common irregular meshes reveal that the rate-distortion performance of the coder is significantly superior to the coding system proposed in [8].
  • Yayın
    Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems
    (IEEE, 2005) Bayazıt, Uluğ; Orcay, Özgür; Konur, Umut; Gürgen, Sadık Fikret
    A large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems.