Hologram Streaming Based on Complex-Domain Rescaling and Optimal Real/Imaginary Bitrate Allocation

Rapid advances in optical imaging have accelerated the emergence of holographic applications. To enable a genuine holographic telepresence experience, the challenge of transmitting massive volumes of holographic data over bandwidth-limited networks must be addressed. This paper presents a hologram streaming framework that combines complex-domain rescaling and bitrate allocation tailored to the complex-valued nature of holograms. First, we build a complex-valued rescaling neural network to reduce the required transmission bandwidth. It consists of a downscaling model for reducing hologram volume and a compression-robust upscaling model for recovering high-resolution holograms. The two models are jointly trained to optimize hologram reconstruction quality and are then deployed separately on the server and client. Second, we develop a distortion model that characterizes the different contributions of the real and imaginary (R/I) components to the amplitude distortion of reconstructed holograms. The model is used in a rate-distortion optimization framework to guide optimal R/I bitrate allocation subject to a bandwidth constraint. Experiments on immersive video datasets show that the proposed framework substantially improves rate-distortion performance; under the BD-rate metric, the baselines require 49.83%–222.71% more bitrate to achieve comparable reconstruction quality.