A DASH-based 3D multi-view video rate control system
Bibliographic record
Abstract
In this paper, we propose a dynamic adaptive rate control system and its associated rate-distortion model for multiview 3D video transmission, which will improve the user's quality of experience in the face of varying network bandwidth. Our rate control system has been built on top of two state-of-the-art key technologies: High Efficiency Video coding (HEVC), and MPEG's Dynamic Adaptive Streaming over HTTP (DASH). We show how to prepare the content at the server side and present a policy for the client to choose content from the server based on our distortion model for views reconstruction. The proposed system is tested under different network conditions. We also provide a user-based test for subjective evaluation of the rendered views, to decide on the number of views and quality of each view to be encoded for a specific network condition. The results of our simulation and the subjective test show that our proposed rate control system for 3D multi-view video allows for the transmission of different bitstreams at a higher quality, compared to non-dynamic adaptive rate control, given network bandwidth fluctuations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".