Predictive and Robust Field-of-View Selection for Virtual Reality Video Streaming
Bibliographic record
Abstract
Virtual reality technology is rapidly evolving towards providing immersive user experience. By predicting user’s field-of-view (FoV) in advance, only transmitting content viewed by the user can help to meet the stringent requirements of delivering enhanced video quality. In this paper, a predictive and robust FoV selection algorithm is devised to dynamically identify a subset of video tiles, guided by the prediction error due to user’s stochastic head movement. Considering that the required data size to cover actual FoV is positively correlated with the prediction error, we construct a context space represented by the prediction error. A partition method of context space is exploited to discretize continuous context, where the prediction errors are classified effectively, and adaptive tile selection can be carried out. Then, a padding strategy is proposed by estimating the transmission gain of each tile in different prediction context, which improves the coverage of transmitted content around the true FoV at less bandwidth cost. Experimental results based on a real-world dataset demonstrate that the proposed algorithm can achieve dynamic FoV adjustment, and effectively improve user’s quality of experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".