Deep learning-based approach for efficient generation and transmission of high-definition computer-generated holography

Jidong Jia; Zhenxing Dong; Yuye Ling; Yan Li; Yikai Su

doi:10.1117/12.3000442

13 March 2024 Deep learning-based approach for efficient generation and transmission of high-definition computer-generated holography

Jidong Jia, Zhenxing Dong, Yuye Ling, Yan Li, Yikai Su

Author Affiliations +

Proceedings Volume 12908, Advances in Display Technologies XIV; 1290808 (2024) https://doi.org/10.1117/12.3000442
Event: SPIE OPTO, 2024, San Francisco, California, United States

Abstract

Deep learning-based computer-generated holography (CGH) has recently demonstrated tremendous potential in three-dimensional (3D) displays and yielded impressive display quality. However, current CGH techniques are mostly limited on generating and transmitting holograms with a resolution of 1080p, which is far from the ultra-high resolution (16K+) required for practical virtual reality (VR) and augmented reality (AR) applications to support a wide field of view and large eye box. One of the major obstacles in current CGH frameworks lies in the limited memory available on consumer-grade GPUs which could not facilitate the generation of highdefinition holograms. Moreover, the existing hologram compression rate can hardly permit the transmission of high-resolution holograms over a 5G communication network, which is crucial for mobile application. To overcome the aforementioned challenges, we proposed an efficient joint framework for hologram generation and transmission to drive the development of consumer-grade high-definition holographic displays. Specifically, for hologram generation, we proposed a plug-and-play module that includes a pixel shuffle layer and a lightweight holographic super-resolution network, enabling the current CGH networks to generate high-definition holograms. For hologram transmission, we presented an efficient holographic transmission framework based on foveated rendering. In simulations, we have successfully achieved the generation and transmission of holograms with a 4K resolution for the first time on an NVIDIA GeForce RTX 3090 GPU. We believe the proposed framework could be a viable approach for the evergrowing data issue in holographic displays.

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Jidong Jia, Zhenxing Dong, Yuye Ling, Yan Li, and Yikai Su "Deep learning-based approach for efficient generation and transmission of high-definition computer-generated holography", Proc. SPIE 12908, Advances in Display Technologies XIV, 1290808 (13 March 2024); https://doi.org/10.1117/12.3000442

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KEYWORDS

Holograms

Computer generated holography

Image compression

3D image reconstruction

Holographic displays

Spatial light modulators

Holography

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