A real-time transport protocol for image-based rendering over heterogeneous wireless networks
Bibliographic record
Abstract
With the recent development of wireless communications and multimedia systems, 3D image-based scenes with photo-realistic rendered images and rendering performance have recently received a great deal of interests.In this paper, we focus upon 3D scenes streaming over heterogeneous wireless communication networks, and we propose a real-time transport protocol for streaming 3D scenes based rendered by image morphing. Our approach is based upon 2D images (i.e., subfunction of plenoptic function) which are taken as a representation of 3D scenes while the view morphing is used to render new images. Based on these assumptions, we have designed the real-time transport protocol (RTP) payload format and packetization schemes for streaming 3D image-based scenes. Furtheremore, in order to enhance the robutness of our streaming mechanism, a special packetization scheme has been developed and a feedback mechanism is proposed to deal wih the drastic changes of the wireless network bandwidth using a periodic feedback schema within the Real-time Control Protocol (RTCP).We discuss our proposed protocol and present an extensive set of simulation experiments to evaluate the performance of our protocol using a variery of real world senarios.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".