High-Speed FHD Full-Color Video Computer-Generated Holography

2025-08-27

AI Summary

To address two critical bottlenecks in high-speed full-color computer-generated holography (CGH)—chromatic crosstalk and insufficient spatiotemporal modeling—this paper proposes Spectrum-Guided Deep Demultiplexing (SGDDM) and HoloMamba, a lightweight asymmetric Mamba-Unet architecture. SGDDM imposes spectral constraints on phase distribution to suppress chromatic crosstalk induced by depth-based demultiplexing at high frame rates, thereby breaking the intrinsic trade-off between frame rate and color fidelity. HoloMamba is the first CGH framework to integrate the state-space model (Mamba) for explicit spatiotemporal correlation modeling, enabling end-to-end efficient hologram generation. Experiments demonstrate that SGDDM ensures high-fidelity full HD (FHD) full-color holographic display, while HoloMamba achieves over 260 FPS for 1080p holographic video generation—surpassing state-of-the-art methods by more than 2.6× in speed.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Computer-generated holography (CGH) is a promising technology for next-generation displays. However, generating high-speed, high-quality holographic video requires both high frame rate display and efficient computation, but is constrained by two key limitations: ($i$) Learning-based models often produce over-smoothed phases with narrow angular spectra, causing severe color crosstalk in high frame rate full-color displays such as depth-division multiplexing and thus resulting in a trade-off between frame rate and color fidelity. ($ii$) Existing frame-by-frame optimization methods typically optimize frames independently, neglecting spatial-temporal correlations between consecutive frames and leading to computationally inefficient solutions. To overcome these challenges, in this paper, we propose a novel high-speed full-color video CGH generation scheme. First, we introduce Spectrum-Guided Depth Division Multiplexing (SGDDM), which optimizes phase distributions via frequency modulation, enabling high-fidelity full-color display at high frame rates. Second, we present HoloMamba, a lightweight asymmetric Mamba-Unet architecture that explicitly models spatial-temporal correlations across video sequences to enhance reconstruction quality and computational efficiency. Extensive simulated and real-world experiments demonstrate that SGDDM achieves high-fidelity full-color display without compromise in frame rate, while HoloMamba generates FHD (1080p) full-color holographic video at over 260 FPS, more than 2.6$ imes$ faster than the prior state-of-the-art Divide-Conquer-and-Merge Strategy.

Problem

• Addresses color crosstalk in high frame rate full-color holographic displays
• Overcomes computational inefficiency in frame-by-frame hologram optimization
• Resolves trade-off between frame rate and color fidelity in CGH

Innovation

• SGDDM optimizes phase via frequency modulation
• HoloMamba uses Mamba-Unet for spatiotemporal modeling
• Achieves 260 FPS FHD holographic video generation