Gaze-contingent efficient hologram compression for foveated near-eye holographic displays

Abstract

Deep learning-based computer-generated holography (CGH) has developed rapidly and yielded remarkable outcomes. However, CGH rendering technologies are confronted with a significant challenge in transmitting massive holograms, which hinders the development of lightweight wearable near-eye holographic displays. Currently, hologram compression frameworks share a resemblance with image compression methods, which fail take into account the human visual system in practical near-eye displays, limiting the improvement of compression efficiency. Herein, we presented an efficient holographic compression framework based on foveated rendering, where we transmitted a high-resolution foveal region at a low compression rate and a low-resolution peripheral region at a high compression rate with dramatically reduced pixel numbers. Our method achieved a compression rate of 40× for a hologram resolution of 1024 × 1024, which represents a twofold increase in compression rate compared to the state-of-the-art (SOTA) method with a PSNR of ∼28.8 dB in the foveal image. Moreover, we further demonstrated the effectiveness of the proposed method in the optical experiment. We believe the proposed approach could be a viable remedy for the evergrowing data issue in near-eye holographic displays.