Bibliographic record
Abstract
3D video applications are growing increasingly common as the required infrastructure and technology to stream 3D video becomes more predominant.However, the quality of displayed videos may fluctuate due to packet failure as an integral part of either wired or wireless streaming networks.Therefore, more robust methods of video streaming have always been fascinating to show more favourable efficiency outcomes.This thesis first examines different video streaming techniques and compares the pros and cons of each technique.It then introduces a new streaming method that applies to 3D video for live video streaming applications especially for a sporting event or other live video applications.To this end, the thesis describes how a 3D video is captured and represented, and how humans perceive the 3D scene.Considering the pros and cons of current video streaming techniques and intended applications, the proposed method introduces a new multiple description coding (MDC) method focusing on interesting objects of the scene, called the region of interest (ROI).It is worth mentioning that a new technique, using the scene's depth information, is used to extract the ROI.This technique is not as complex as learning algorithms are, and there is no need to train the algorithm.Since the human eye is more sensitive to objects than pixels, this method can also provide better performance from the point of subjective assessment (which is out of focus of this thesis) because the proposed method focuses on important objects of the scene and assigns more bandwidth to them.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".