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DOI: 10.14569/IJACSA.2017.080561
PDF

High Precision DCT CORDIC Architectures for Maximum PSNR

Author 1: Imen Ben Saad
Author 2: Sonia Mami
Author 3: Yassine Hachaichi
Author 4: Younes Lahbib
Author 5: Abdelkader Mami

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 5, 2017.

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Abstract: This paper proposes two optimal Cordic Loeffler based DCT (Discrete Cosine Transform algorithm) architectures: a fast and low Power DCT architecture and a high PSNR DCT architecture. The rotation parameters of CORDIC angles required for these architectures have been calculated using a MATLAB script. This script allows the variation of the angle’s precision from 10-1 to 10-4. The experimental results show that the fast and low Power DCT architecture correponds to the precision 10-1. Its complexity is even lower than the BinDCT which is a reference in terms of low complexity and its power has been enhanced in comparison with the conventional Cordic Loeffler DCT by 12 mW. The experimental results also show that the high PSNR DCT architecture corresponds to the precision 10-3 for which the PSNR has been improved by 6.55 dB in comparison with the conventional Cordic Loeffler DCT. Then, the hardware implementation and the generated RTL of some required Cordics are presented.

Keywords: Cordic Loeffler DCT; high quality architecture; low power architecture; Image Processing; DCT

Imen Ben Saad, Sonia Mami, Yassine Hachaichi, Younes Lahbib and Abdelkader Mami, “High Precision DCT CORDIC Architectures for Maximum PSNR” International Journal of Advanced Computer Science and Applications(IJACSA), 8(5), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080561

@article{Saad2017,
title = {High Precision DCT CORDIC Architectures for Maximum PSNR},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080561},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080561},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {5},
author = {Imen Ben Saad and Sonia Mami and Yassine Hachaichi and Younes Lahbib and Abdelkader Mami}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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