# JPEG-Inspired Cloud-Edge Holography

arXiv: https://arxiv.org/abs/2512.12367 · HTML: https://mlsanity.com/paper/2512.12367/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [Time-multiplexed Neural Holography: A flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators](https://mlsanity.com/paper/2205.02367/) (2022)
- [Neural 'Etendue Expander for Ultra-Wide-Angle High-Fidelity Holographic Display](https://mlsanity.com/paper/2109.08123/) (2021)
- [Learning to Improve Image Compression without Changing the Standard Decoder](https://mlsanity.com/paper/2009.12927/) (2020)
- [Compression of phase-only holograms with JPEG standard and deep learning](https://mlsanity.com/paper/1806.03811/) (2018)
- [Divide-Conquer-and-Merge: Memory- and Time-Efficient Holographic Displays](https://mlsanity.com/paper/2404.10777/) (2024)
- [A Lightweight Complex-Valued Deformable CNN for High-Quality Computer-Generated Holography](https://mlsanity.com/paper/2506.14542/) (2025)

## Abstract (arXiv)

Computer-generated holography (CGH) presents a transformative solution for near-eye displays in augmented and virtual reality. Recent advances in deep learning have greatly improved CGH in reconstructed quality and computational efficiency. However, deploying neural CGH pipelines directly on compact, eyeglass-style devices is hindered by stringent constraints on computation and energy consumption, while cloud offloading followed by transmission with natural image codecs often distorts phase information and requires high bandwidth to maintain reconstruction quality. Neural compression methods can reduce bandwidth but impose heavy neural decoders at the edge, increasing inference latency and hardware demand. In this work, we introduce JPEG-Inspired Cloud-Edge Holography, an efficient pipeline designed around a learnable transform codec that retains the block-structured and hardware-friendly nature of JPEG. Our system shifts all heavy neural processing to the cloud, while the edge device performs only lightweight decoding without any neural inference. To further improve throughput, we implement custom CUDA kernels for entropy coding on both cloud and edge. This design achieves a peak signal-to-noise ratio of 32.15 dB at < 2 bits per pixel with decode latency as low as 4.2 ms. Both numerical simulations and optical experiments confirm the high reconstruction quality of the holograms. By aligning CGH with a codec that preserves JPEG's structural efficiency while extending it with learnable components, our framework enables low-latency, bandwidth-efficient hologram streaming on resource-constrained wearable devices-using only simple block-based decoding readily supported by modern system-on-chips, without requiring neural decoders or specialized hardware.