Impact of Image Resolution on Controlling Drones Using Remote VR Headset Visualization and a Cloud Architecture
Bibliographic record
Abstract
This paper investigates the critical relationship between image resolution, network conditions, and system latency in remote visualization of drone camera and control systems. We present an analysis of how these factors affect the real-time control and visualization capabilities in a system architecture consisting of a consumer-grade drone, controller application, server, and visualization interface (computer and virtual reality). Experiments were conducted across multiple image resolutions (240p to 4K) and three network conditions (Unthrottled bandwidth, Fast 4G, and 3G). Results demonstrate that higher resolutions significantly increase image transmission time, with 4K resolution requiring 231.1ms compared to 69.3ms for 360p under unthrottled network conditions. Lower network performance levels dramatically impact performance, with 1080p transmission increasing from 169.7ms on unthrottled network to 321s on 3G networks. Given drones are moving through space, the latency measurements are converted to the physical distance travelled by the drone during the transmission at various movement speeds (0.5-8 m/s). These provide practical metrics for system usability relating to the potential for operators to not see or be able to maneuver around obstacles due to untimely visualizations. Our findings offer valuable insights for designing real-time drone visualization systems that provide and adapt the balance image quality with latency constraints across varying network environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".