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

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.

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

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.

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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