Chercheur Doctorant Semantic Data Compression for Extended Reality in Aeronautic Industry H/F CESI

Thesis title: Semantic Data Compression for Extended Reality in Aeronautic Industry

Compression sémantiques de données pour la réalité étendue dans le secteur aéronautique

Scientific Fields

• Extended Reality Systems & Human-Computer Interaction (HCI)

• Edge-Cloud Computing and Distributed Systems

• Industrial Internet of Things (IIoT)

• Signal Processing and Semantic Data Compression

Keywords

• Data Compression

• Extended Reality (XR)

• Edge-Cloud Computing

• Real-Time Streaming

• Machine Learning

Supervision

Thesis Director

• Samir OUCHANI, PhD., HDR., Research Director, Aix-en-Provence, France.

Thesis Supervisor

• Hugues Marie KAMDJOU, PhD., Associate Professor

Acknowledgment

This work is conducted as part of Campus Aero Adour (C2A) project funded by the government under the France 2030 Plan.

1

Research Work

Abstract

Aeronautical eXtended Reality (XR) deployments (maintenance assistance, inspection, and training) increasingly rely on rich 3D scenes, CAD/digital-twin assets, and multi-sensor streams that must be delivered in real time to mobile headsets. In operational settings, network connectivity is often unstable (hangars, tarmacs, factories), while XR devices remain constrained in compute, memory, battery, and network bandwidth. Classical compression and streaming reduce bitrate but treat all content uniformly, failing to prioritize task-critical elements (e.g., safety-relevant parts, occluded components, procedural cues). This gap motivates semantic, edge-assisted data compression strategies that reduce communication cost while preserving the information that directly impacts industrial performance and user effectiveness.

This thesis addresses trustworthy semantic compression to enable real-time XR in aeronautics under strict bandwidth, latency, and device-energy constraints. The method proposes task-aware representations that prioritize the transmission of operational meaning over uniform raw pixels or geometry. In this approach, what matters encompasses critical objects, procedural cues, real-time IIoT data essential for situated guidance, but also discrepancies between the real system and its digital twin, specifically highlighting structural discrepancies such as component presence/absence or geometric deviations. Compression decisions are optimized and executed at the edge to reduce end-to-end delay and stabilize user-perceived quality on resource-limited XR headsets [1, 2]. The resulting pipeline couples semantic inference with adaptive coding and streaming to minimize transmitted data while preserving operational accuracy.

PhD project

Scientific Context

Industry 5.0 promotes human-centric, resilient, and sustainable production systems, where XR supports situated guidance, remote collaboration, and digital-twin interaction in safety-critical domains such as aeronautics. Achieving stable and low-latency XR requires continuous pose tracking, 3D scene updates, and multi-sensor fusion, often under fluctuating network conditions and strict operational constraints. Yet, current head-mounted displays (HMDs) remain limited in CPU/GPU capability, memory, energy, and thermal budget, which restricts on-device inference and high-fidelity rendering at scale. Consequently, conventional end-to-end pipelines that stream dense geometry, textures, and video frames are prone to bandwidth bottlenecks and motion-to-photon delays that degrade user performance and comfort in dynamic Industrial IoT (IIoT) environments [3, 4].

Semantic data compression has emerged as a promising direction to bridge this gap by encoding meaningful structures (objects, parts, relations, and task-relevant regions) rather than uniformly compressing raw signals. Recent studies show that semantic-aware representations can significantly improve transmission efficiency in industrial settings by prioritizing critical content and reducing redundancy [5, 6]. In this thesis context, deep learning-driven semantic analysis is leveraged to identify what is operationally important (e.g., aircraft components, procedural cues, hazard zones) and to drive adaptive compression and streaming decisions. Beyond bitrate reduction, this paradigm aims to preserve task accuracy while improving end-to-end responsiveness, thereby enhancing both Quality of Service (QoS) and Quality of Experience (QoE) for XR users in aeronautical workflows [2, 7].

Thesis subject

This PhD thesis targets the design of a robust semantic data compression pipeline for real-time XR in IIoT, with a particular focus on aeronautical workflows. The thesis will deliver an end-to-end method that (i) models and extracts semantics from XR scenes/datasets, (ii) performs adaptive compression/aggregation driven by these semantics, and (iii) validates the approach under realistic network and device constraints. The key objectives of this PhD thesis include:

• Semantic workload characterization: Analyze the semantic data generated by aeronautic industrial XR

pipelines (poses, object states, part-level relations, interactions, annotations, and context) to determine

what must be preserved for task performance and what can be reduced through compression or aggregation.

• Semantic-aware compression and aggregation: Design and implement efficient Machine Learning (ML) algorithms that jointly exploit semantic structure and temporal redundancy to optimize the uplink/downlink between HMDs and edge servers. This includes learning-based importance prediction (e.g., task-aware ROI/part importance), semantic quantization, and progressive/partial transmission strategies.

• Edge-assisted decision making: Investigate edge/cloud orchestration where semantic inference and compression policy selection are offloaded to edge servers, reducing HMD compute/energy usage while meeting real-time constraints (motion-to-photon delay, frame stability, and reliable pose updates).

• Rigorous evaluation and benchmarking: Conduct extensive experiments to assess (i) communication efficiency (bitrate, packet loss sensitivity), (ii) QoS (end-to-end latency, frame rate stability, pose/estimation accuracy), (iii) server-side cost (CPU/GPU utilization, memory footprint, scalability), and (iv) user- centric QoE (perceived quality, comfort, task completion time/error rate) in IIoT-like conditions.

• Prototype integration in an aeronautical XR setting: Integrate the proposed methods into an XR prototype (HMD + edge) and validate performance on representative aeronautical scenarios (maintenance assistance, inspection guidance, or training), demonstrating deployability and measurable gains over baseline codecs and non-semantic compression.

Previous Work in the Laboratory

In this context, several research works and projects have been conducted in CESI LINEACT laboratory. These include, among others:

• Resource-constrained eXtended Reality operated with digital twin in IIoT [1], [8].

• JENII project, is a remote…

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