TYLE: Tile-based Dynamic Quality Enhancement for 360-degree Video Streaming
Bibliographic record
Abstract
In recent years, live streaming of 360° videos has increased in popularity due to the emergence of virtual and mixed reality (VR/MR) applications. The high quality and bird-eye view characteristics of VR/MR pose real-time challenges for 360° video streaming. The tile-based 360° video streaming has been created based on Dynamic Adaptive Streaming over HTTP (DASH), mainly focusing on adaptation algorithms but ignoring the impact of coding properties and variations in video content on streaming quality. In this paper, we take on the challenge in a new direction through the exploration of the potential of CRF rate control. Our deep quality inspection of CRF transcoded videos lead to the proposal of a dynamic quality ladder for a more continuous quality provisioning in contrast to the conventional discrete quality levels. Our quality selection is based on visual quality and CRF rate control rather than just the resolution and bitrate in conventional DASH. We propose TYLE, a Tile-based dynamic quality enhancement for 360° video streaming. Without increasing the bandwidth demand, TYLE serves the content in the Field-of-View (FoV) at the highest quality level and content in the near-FoV region with improved visual quality compare to conventional DASH. TYLE maintains smoother and stabler playback, especially under the condition challenged by bandwidth under-provisioning and high motion-activity videos.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".