BTF Compression via Sparse Tensor Decomposition

Abstract

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.