Low-Complexity Multidimensional DCT Approximations for High-Order Tensor Data Decorrelation.
- DOI
- 10.1109/tip.2017.2679442
- Published
- 2017 May
- Container
- IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tip.2017.2679442,
title = {Low-Complexity Multidimensional DCT Approximations for High-Order Tensor Data Decorrelation.},
author = {de A Coutinho VA and Cintra RJ and Bayer FM},
year = {2017},
journal = {IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
doi = {10.1109/tip.2017.2679442},
url = {https://doi.org/10.1109/tip.2017.2679442}
}RIS
TY - JOUR TI - Low-Complexity Multidimensional DCT Approximations for High-Order Tensor Data Decorrelation. AU - de A Coutinho VA AU - Cintra RJ AU - Bayer FM PY - 2017 JO - IEEE transactions on image processing : a publication of the IEEE Signal Processing Society DO - 10.1109/tip.2017.2679442 UR - https://doi.org/10.1109/tip.2017.2679442 ER -
APA
VA, D. A. C., RJ, C., & FM, B. (2017). Low-Complexity Multidimensional DCT Approximations for High-Order Tensor Data Decorrelation.. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. https://doi.org/10.1109/tip.2017.2679442
Source records
- pubmed · retrieved 2026-09-25T04:03:47.253Z