Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/147674
 Title: Low-Complexity Multidimensional DCT Approximations for High-Order Tensor Data Decorrelation Authors: V&x00ED;tor de A. Coutinho;Renato J. Cintra;F&x00E1;bio M. Bayer Year: 2017 Publisher: IEEE Abstract: In this paper, we introduce low-complexity multidimensional discrete cosine transform (DCT) approximations. 3D DCT approximations are formalized in terms of high-order tensor theory. The formulation is extended to higher dimensions with arbitrary lengths. Several multiplierless 8\times 8 \times 8 approximate methods are proposed and the computational complexity is discussed for the general multidimensional case. The proposed methods complexity cost was assessed, presenting considerably lower arithmetic operations when compared with the exact 3D DCT. The proposed approximations were embedded into 3D DCT-based video coding scheme and a modified quantization step was introduced. The simulation results showed that the approximate 3D DCT coding methods offer almost identical output visual quality when compared with exact 3D DCT scheme. The proposed 3D approximations were also employed as a tool for visual tracking. The approximate 3D DCT-based proposed system performs similarly to the original exact 3D DCT-based method. In general, the suggested methods showed competitive performance at a considerably lower computational cost. URI: http://localhost/handle/Hannan/147674 volume: 26 issue: 5 More Information: 2296,2310 Appears in Collections: 2017

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