BTF Compression via Sparse Tensor Decomposition

In: Computer Graphics Forum (Proc. of EGSR) (Juli 2009), 28:4(1181-1188)
 

Abstract

In this paper, we present a novel compression technique for Bidirectional Texture Functions based on a sparse tensor decomposition. We apply the K-SVD algorithm along two different modes of a tensor to decompose it into a small dictionary and two sparse tensors. This representation is very compact, allowing for considerably better compression ratios at the same RMS error than possible with current compression techniques like PCA, N-mode SVD and Per Cluster Factorization. In contrast to other tensor decomposition based techniques, the use of a sparse representation achieves a rendering performance that is at high compression ratios similar to PCA based methods.

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Bibtex

@ARTICLE{ruiters-2009-ksvd,
    author = {Ruiters, Roland and Klein, Reinhard},
     pages = {1181--1188},
     title = {BTF Compression via Sparse Tensor Decomposition},
   journal = {Computer Graphics Forum (Proc. of EGSR)},
    volume = {28},
    number = {4},
      year = {2009},
     month = jul,
  abstract = {In this paper, we present a novel compression technique for Bidirectional Texture Functions based on
              a sparse tensor decomposition. We apply the K-SVD algorithm along two different modes of a tensor to
              decompose it into a small dictionary and two sparse tensors. This representation is very compact,
              allowing for considerably better compression ratios at the same RMS error than possible with current
              compression techniques like PCA, N-mode SVD and Per Cluster Factorization. In contrast to other
              tensor decomposition based techniques, the use of a sparse representation achieves a rendering
              performance that is at high compression ratios similar to PCA based methods.}
}