|Autors: Kountchev, R. K., Mironov, R. P., Kountcheva R.|
Title: Complexity Evaluation of Tensor Decomposition Using 3D Inverse Spectrum Pyramid in respect of Deterministic Orthogonal Transforms
Keywords: Deterministic pyramidal decompositions, Computational complexity, Recursive calculation, 3D Inverse Spectrum Pyramid, 3D Walsh-Hadamard transform
Abstract: Recently, orthogonal 3D tensor decompositions are widely involved in the processing of various kinds of 3D data such as multimedia signals, correlated image sequences, etc. The methods for tensor decomposition could be divided into two main groups: statistical, based on various modifications of the Principal Component Analysis and the Singular Value Decomposition, and deterministic, based on the pyramidal 3D Discrete Wavelet Transform decompositions, Curvelet/Contourlet Discrete Transform and the Shearlet Discrete Transform. The methods from the first group surpass these from the second in the higher decorrelation of the decomposition components, but these from the second group have much lower computational complexity. In this work are compared the structures and is evaluated the computational complexity of the tensor decompositions, mentioned above, with the decomposition developed by the authors, which is based on the Reduced 3D Inverse Spectrum Pyramid (3D-RISP).
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Вид: статия в списание, публикация в реферирано издание, индексирана в Google Scholar