Performance evaluation of a streaming based protocol for 3D virtual environment exploration on mobile devices
Bibliographic record
Abstract
With the recent advances of mobile computing devices with communication capabilities, virtual environment walkthrough and real-time streaming movie playback for mobile devices has opened a new class of 3D virtual environment exploration based applications such as virtual guides and malls, online gaming, training and monitoring, just to name a few. The challenge lies in how to provide a rich and detailed 3D virtual environment on thin mobile devices that are known for their lack of proper resources to process large scale 3D geometric data. In this paper, we focus on defining a new approach to remote rendering and interactive visualization of 3D virtual environments on thin mobile devices such as PDAs and cell phones. To this end, we propose efficient end-to-end streaming and rate control protocols to support the requirements of bandwidth-demanding multimedia systems. The main purpose of our proposed rate control scheme is to achieve both high end-to-end throughput and low frame rate ºuctuation in order to adapt data traffic to the frequent changes of the bandwidth and error rate, mainly due to the nature of wireless networks. We discuss the design of our proposed streaming and rate control algorithms, and report on the their performance evaluation using an extensive set of simulation experiments.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".