Hierarchical Tensor Approximation of Multi-Dimensional Visual Data
Visual data comprise of multi-scale and inhomogeneous signals. This paper exploits these characteristics and develops a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. The hierarchical tensor approximation supports progressive transmission and partial decompression.