Anchor View Allocation for Collaborative Free Viewpoint Video Streaming
Bibliographic record
Abstract
In free viewpoint video, a viewer can choose at will any camera angle or the so-called “virtual view” to observe a dynamic 3-D scene, enhancing his/her depth perception. The virtual view is synthesized using texture and depth videos of two anchor camera views via depth-image-based rendering (DIBR). We consider, for the first time, collaborative live streaming of a free viewpoint video, where a group of users may interactively pull and cooperatively share streams of different anchor views. There is a cost to access the anchor views from the live source, a cost to “reconfigure” the peer network due to a change in selected anchors during view switching, and a distortion cost due to the distance of the virtual views to the received anchor views at users. We optimize the anchor views allocated to users so as to minimize the overall streaming cost given by the access cost, reconfiguration cost, and view distortion cost. We first show that, if the reconfiguration cost due to view switching is negligible, the view allocation problem can be optimally and efficiently solved in polynomial time using dynamic programming. For the case of non-negligible reconfiguration cost, the problem becomes NP-hard. We thus present a locally optimal and centralized algorithm inspired by Lloyd's algorithm used in non-uniform scalar quantization. We further propose a distributed algorithm with convergence guarantee, where each peer group independently makes merge-and-split decisions with a well-defined fairness criteria. Simulation results show that our algorithms achieve low streaming cost due to its excellent anchor view allocation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".