Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network

Jingyuan Ma; Guanglin Yang; Haiyan Xie

doi:doi:10.11648/j.ajop.20231101.11

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Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network

Jingyuan Ma, Guanglin Yang, Haiyan Xie

Published in American Journal of Optics and Photonics (Volume 11, Issue 1)

Received: 5 August 2023 Accepted: 28 August 2023 Published: 14 September 2023

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Abstract

In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.

Published in	American Journal of Optics and Photonics (Volume 11, Issue 1)
DOI	10.11648/j.ajop.20231101.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Computer Holography, Image Compression, Neural Network, Quantum Computing, Image Reconstruction

References

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APA Style

Jingyuan Ma, Guanglin Yang, Haiyan Xie. (2023). Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. American Journal of Optics and Photonics, 11(1), 1-9. https://doi.org/10.11648/j.ajop.20231101.11

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ACS Style

Jingyuan Ma; Guanglin Yang; Haiyan Xie. Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. Am. J. Opt. Photonics 2023, 11(1), 1-9. doi: 10.11648/j.ajop.20231101.11

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AMA Style

Jingyuan Ma, Guanglin Yang, Haiyan Xie. Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. Am J Opt Photonics. 2023;11(1):1-9. doi: 10.11648/j.ajop.20231101.11

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@article{10.11648/j.ajop.20231101.11,
  author = {Jingyuan Ma and Guanglin Yang and Haiyan Xie},
  title = {Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network},
  journal = {American Journal of Optics and Photonics},
  volume = {11},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajop.20231101.11},
  url = {https://doi.org/10.11648/j.ajop.20231101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajop.20231101.11},
  abstract = {In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.},
 year = {2023}
}

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TY  - JOUR
T1  - Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network
AU  - Jingyuan Ma
AU  - Guanglin Yang
AU  - Haiyan Xie
Y1  - 2023/09/14
PY  - 2023
N1  - https://doi.org/10.11648/j.ajop.20231101.11
DO  - 10.11648/j.ajop.20231101.11
T2  - American Journal of Optics and Photonics
JF  - American Journal of Optics and Photonics
JO  - American Journal of Optics and Photonics
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2330-8494
UR  - https://doi.org/10.11648/j.ajop.20231101.11
AB  - In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.
VL  - 11
IS  - 1
ER  -

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Author Information

Jingyuan Ma

Laboratory of Signal and Information Processing, School of Electronics, Peking University, Beijing, China
Guanglin Yang

Laboratory of Signal and Information Processing, School of Electronics, Peking University, Beijing, China
Haiyan Xie

China Science Patent and Trademark Agent, Beijing, China

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Plain Text BibTeX RIS

APA Style

Jingyuan Ma, Guanglin Yang, Haiyan Xie. (2023). Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. American Journal of Optics and Photonics, 11(1), 1-9. https://doi.org/10.11648/j.ajop.20231101.11

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ACS Style

Jingyuan Ma; Guanglin Yang; Haiyan Xie. Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. Am. J. Opt. Photonics 2023, 11(1), 1-9. doi: 10.11648/j.ajop.20231101.11

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AMA Style

Jingyuan Ma, Guanglin Yang, Haiyan Xie. Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network. Am J Opt Photonics. 2023;11(1):1-9. doi: 10.11648/j.ajop.20231101.11

Copy | Download

@article{10.11648/j.ajop.20231101.11,
  author = {Jingyuan Ma and Guanglin Yang and Haiyan Xie},
  title = {Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network},
  journal = {American Journal of Optics and Photonics},
  volume = {11},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ajop.20231101.11},
  url = {https://doi.org/10.11648/j.ajop.20231101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajop.20231101.11},
  abstract = {In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Compressing Color Computer-Generated Hologram Using Gradient Optimized Quantum-Inspired Neural Network
AU  - Jingyuan Ma
AU  - Guanglin Yang
AU  - Haiyan Xie
Y1  - 2023/09/14
PY  - 2023
N1  - https://doi.org/10.11648/j.ajop.20231101.11
DO  - 10.11648/j.ajop.20231101.11
T2  - American Journal of Optics and Photonics
JF  - American Journal of Optics and Photonics
JO  - American Journal of Optics and Photonics
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2330-8494
UR  - https://doi.org/10.11648/j.ajop.20231101.11
AB  - In the existing electronic communication systems, fast transmission of three-dimensional image information requires compression and encoding of holographic images. In this paper, a method for compressing the color computer-generated hologram by the quantum-inspired neural network based on the gradient optimized algorithm is proposed. By optimizing the gradient descent calculation method of quantum-inspired neural network, the convergence speed of the quantum-inspired neural network was improved, and the loss error of the quantum-inspired neural network was reduced. The bandwidth-limited angular spectrum method was used to calculate the color double-phase computer-generated hologram. Gradient optimized quantum-inspired neural networks and traditional quantum-inspired neural networks are used to compress the color double-phase computer-generated hologram respectively, and the decompressed color double-phase computer-generated hologram is reconstructed to the original color image by the angular spectrum method. It is shown that gradient-optimized quantum-inspired neural networks have better results in compressing and reconstructing color computer-generated holograms, which obtain high-quality and low color difference reconstructed original images compared to traditional quantum-inspired neural networks. Different gradient optimization algorithms also have differences in the training of computer-generated holograms at different wavelengths. Therefore, suitable gradient-optimized quantum-inspired neural networks can accelerate the compression speed of computer-generated holograms, while improving the quality of decompressed computer-generated holograms and reconstructed original images.
VL  - 11
IS  - 1
ER  -

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